by year by topic

My Google Scholar Page

2025

• Source free domain adaptation with pseudo-labeling quality assessed by SAM in Fundus image segmentation, Bar Yaacovi and Jacob Goldberger, IEEE International Symposium on Biomedical Imaging (ISBI), 2025.
• Clinical measurements with calibrated instance-dependent condence interval, Rotem Nizhar, Lior Frenkel and Jacob Goldberger, Medical Imaging with Deep Learning (MIDL), 2025.
• Automatic detection of domain shifts in speech enhancement systems using confidence-based metrics, Lior Frenkel, Shlomo E. Chazan and Jacob Goldberger, IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2025.
• Auditory attention decoding based on neural-network for binaural beamforming applications, Roy Gueta, Elana Zion-Golumbic, Jacob Goldberger and Sharon Gannot, Frontiers in Signal Processing, 2025.
•  Conformal prediction of classifiers with many classes based on noisy labels, Coby Penso, Jacob Goldberger and Ethan Fetaya, Symposium on Conformal and Probabilistic Prediction with Applications (COPA), 2025.
•  Privacy-preserving conformal prediction under local differential privacy, Coby Penso, Bar Mahpud, Jacob Goldberger and Or Sheffet, Symposium on Conformal and Probabilistic Prediction with Applications (COPA), 2025.
• Calibrating without labels: source-free conformal prediction using pseudo-labels, Shachar Angelman, Rotem Nizhar and Jacob Goldberger, Symposium on Conformal and Probabilistic Prediction with Applications (COPA), 2025.

2024

• Confidence calibration of a medical imaging classification system that is robust to label noise, Coby Penso, Lior Frenkel and Jacob Goldberger, IEEE Transactions on Medical Imaging, vol. 43(6), pp.  2050-2060,  2024.
• Domain adaptation using suitable pseudo labels for speech enhancement and dereverberation, Lior Frenkel, Shlomi Chazan and Jacob Goldberger, IEEE Transactions on Audio, Speech and Language Processing,  vol.  36. pp.  1226-1236, 2024.
• A conformalized learning of a prediction set with applications to medical imaging classification, Roy Hirsch and Jacob Goldberger, IEEE International Symposium on Biomedical Imaging (ISBI), 2024.
• A joint training and confidence calibration procedure that is robust to label noise, Coby Penso and Jacob Goldberger,  IEEE International Symposium on Biomedical Imaging (ISBI), 2024.
• Characterization of Alternative Splicing in High-Risk Wilms’ Tumors, Yaron Trink, Achia Urbach , Benjamin Dekel, Peter Hohenstein, Jacob Goldberger  and Tomer Kalisky, International Journal of Molecular Sciences 25 (8), 2024.
• The power of summary-source alignments, Ori Ernst, Ori Shapira, Aviv Slobodkin, Sharon Adar, Mohit Bansal, Jacob Goldberger, Ran Levy, and Ido Dagan, Findings of  ACL, 2024.
• De-confusing pseudo-labels in source-free domain adaptation, Idit Diamant, Amir Rosenfeld, Idan Achituve, Jacob Goldberger, Arnon Netzer,  European Conference on Computer Vision (ECCV), 2024.
• Noise-robust conformal prediction for medical image classification, Coby Penso and Jacob Goldberger, MICCAI Int. Workshop on Machine Learning in Medical Imaging (MLMI), 2024.
• Calibration of network confidence for unsupervised domain adaptation using estimated accuracy,  The ECCV workshop on Uncertainty Quantification for Computer Vision, Coby Penso and Jacob Goldberger, 2024.

2023

• An entangled mixture of variational autoencoders approach to deep clustering, Avi Caciularu and Jacob Goldberger, Neurocomputing,  vol. 520,  pp. 182-189,  2023.
• Calibration of a regression network based on the predictive variance with applications to medical images, Lior Frenkel and Jacob Goldberger, IEEE International Symposium on Biomedical Imaging (ISBI), 2023.
• Supervised adaptation by transferring both the parameter set and its gradient,  Shaya Goodman, Hayit Greenspan and Jacob Goldberger,  Neurocomputing, 2023.
• Peek across: Improving multi-document modeling via cross-document question-answering, Avi Caciularu, Matthew Peters, Jacob Goldberger, Ido Dagan and Arman Cohan, Annual Meeting of the Association for Computational Linguistics (ACL), 2023.
• Conformal nucleus sampling,  Shauli Ravfogel, Yoav Goldberg and Jacob Goldberger, Findings of ACL, 2023.
• PLPP: A pseudo labeling post-processing strategy for unsupervised domain adaptation,  Tomer Bar Natan, Hayit Greenspan and Jacob Goldberger, IEEE International Symposium on Biomedical Imaging (ISBI), 2023.
• Characterization of continuous transcriptional heterogeneity in high-risk blastemal-type Wilms’ tumors using unsupervised machine learning, Yaron Trink, Achia Urbach, Benjamin Dekel, Peter Hohenstein, Jacob Goldberger and Tomer Kalisky, International Journal of Molecular Sciences, 24 (4), 2023.
• Domain adaptation for speech enhancement in a large domain gap, Lior Frenkel, Jacob Goldberger and  Shlomo Chazan,  Interspeech, 2023.
• Utilizing perturbation of atoms' positions for equivariant pre-training in 3D molecular analysis.  Tal Kiani, Avi Caciularu, Shani Zev, Dan Thomas Mayor and Jacob Goldberger, IEEE Machine Learning for Signal Processing Workshop (MLSP), 2023.
• PLST: A pseudo-labels with a smooth transition Strategy for medical site adaptation, Tomer Bar Natan, Hayit Greenspan and Jacob Goldberger,  MICCAI  Workshop on Domain Adaptation and Representation Transfer (DART), 2023.

2022

• Calibration of medical imaging classification systems with weight scaling, Lior Frenkel and Jacob Goldberger, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022.
• Supervised domain adaptation using gradient transfer for improved medical image analysis, Shaya Goodman, Hayit Greenspan and Jacob Goldberger,  MICCAI  Workshop on Domain Adaptation and Representation Transfer (DART), 2022.
• Unsupervised site adaptation by intra-site variability alignment,  Shaya Goodman*, Shira Kasten Serlin*, Hayit Greenspan and Jacob Goldberger,  MICCAI  Workshop on Domain Adaptation and Representation Transfer (DART), 2022.
• Long context question answering via supervised contrastive learning, Avi Caciularu, Ido Dagan, Jacob Goldberger and Arman Cohan, The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2022.
• A proposition-level clustering approach for multi-document summarization, Ori Ernst, Avi Caciularu, Ori Shapira, Ramakanth Pasunuru, Mohit Bansal, Jacob Goldberger and Ido Dagan, The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) , 2022.
• Class-based attention mechanism for chest radiograph multi-label categorization, David Sriker, Hayit Greenspan and Jacob Goldberger, IEEE International Symposium on Biomedical Imaging (ISBI), 2022.
• Adaptation of a multisite network to a new clinical site via batch-normalization similarity, Shira Kasten Serlin, Jacob Goldberger and Hayit Greenspan, IEEE International Symposium on Biomedical Imaging (ISBI), 2022.
• Network calibration by temperature scaling based on the predicted confidence, Lior Frenkel and Jacob Goldberger, The European Signal Processing Conference (EUSIPCO), 2022.

2021

• Stochastic weight pruning and the role of regularization in shaping network structure, Yael Ziv, Jacob Goldberger and Tammy Riklin-Raviv, Neurocomputing, vol. 462, pp. 555-567, 2021.
• Summary-source proposition-level alignment: task, datasets and supervised baseline, Ori Ernst, Ori Shapira, Ramakanth Pasunuru, Michael Lepioshkin, Jacob Goldberger, Mohit Bansal and Ido Dagan, Conference on Computational Natural Language Learning (CoNLL), 2021. Best paper runner up.
• Weakly and semi supervised detection in medical imaging via deep dual branch net, Ran Bakalo, Jacob Goldberger and Rami Ben-Ari. Neurocomputing, vol. 421, pp. 15-25, 2021.
• perm2vec: Attentive graph permutation selection for decoding of error correction codes, Avi Caciularu, Nir Raviv, Tomer Raviv, Jacob Goldberger and Yair Be'ery, IEEE J. Selected Areas Communication, vol. 39(1), pp. 79-88, 2021.
• Dynamically localizing multiple speakers based on the time-frequency domain, Hodaya Hammer, Shlomo E. Chazan, Jacob Goldberger and Sharon Gannot, EURASIP Journal on Audio, Speech, and Music Processing, 2021.
• An atlas of classifiers - A machine learning paradigm for brain MRI Segmentation, Shiri Gordon, Boris Kodner, Tal Goldfryd, Michael Sidorov, Jacob Goldberger, Tammy Riklin-Raviv,  Medical, Biological Eng & Computing (MBEC), 2021.
• Factorized CRF with batch normalization based on the entire training data, Eran Goldman and Jacob Goldberger, IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2021.
• Speech enhancement with mixture of deep experts with clean clustering pre-training, Shlomo E. Chazan, Jacob Goldberger and Sharon Gannot, IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2021.
• Denoising word embeddings by averaging in a shared space,  Avi Caciularu,  Ido Dagan and Jacob Goldberger, *SEM: Joint Conference on Lexical and Computational Semantics, 2021.
• Transfer learning with a layer dependent regularization for medical image segmentation, Nimrod Sagie, Hayit Greenspan and Jacob Goldberger, MICCAI Int. Workshop on Machine Learning in Medical Imaging (MLMI), 2021.
• Transfer learning via parameter regularization for medical image segmentation, Nimrod Sagie, Hayit Greenspan and Jacob Goldberger, The European Signal Processing Conference (EUSIPCO), 2021.
• Network calibration by class-based temperature scaling, Lior Frenkel and Jacob Goldberger, The European Signal Processing Conference (EUSIPCO), 2021.

2020

• A locally linear procedure for word translation. Soham Dan, Hagai Taitelbaum and Jacob Goldberger, The International Conference on Computational Linguistics (COLING), 2020.
• Unsupervised distillation of syntactic information from contextualized word representations. Shauli Ravfogel, Yanai Elazar, Jacob Goldberger and Yoav Goldberg, Black-boxNLP EMNLP Workshop, 2020.
• K-Autoencoders deep clustering. Yaniv Opochinsky, Shlomo E. Chazan, Sharon Gannot and Jacob Goldberger, IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2020.
• A composite DNN architecture for speech enhancement. Yochai Yemini, Shlomo E. Chazan, Jacob Goldberger and Sharon Gannot, IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2020.
• Learning probabilistic fusion of multilabel lesion contours. Gal Cohen, Hayit Greenspan and Jacob Goldberger, IEEE International Symposium on Biomedical Imaging (ISBI), 2020.
• CRF with deep class embedding for large scale classification. Eran Goldman and Jacob Goldberger, Computer Vision and Image Understanding (CVIU), vol 191, pp. 1-9, 2020.
• Mixture of views network with applications to multi-view medical imaging. Yaniv Shachor, Hayit Greenspan and Jacob Goldberger, Neurocomputing, vol. 374, pp. 1-9, 2020.

2019

• Multilingual word translation using auxiliary languages. Hagai Taitelbaum, Gal Chechik and Jacob Goldberger, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019.
• A multi-pairwise extension of procrustes analysis for multilingual word translation. Hagai Taitelbaum, Gal Chechik and Jacob Goldberger. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019.
• Deep clustering based on a mixture of autoencoders. Shlomo E. Chazan, Sharon Gannot and Jacob Goldberger, IEEE Machine Learning for Signal Processing Workshop (MLSP), 2019.
• Interpretable online banking fraud detection based on hierarchical attention mechanism. Idan Achituve, Sarit Kraus and Jacob Goldberger, IEEE Machine Learning for Signal Processing Workshop (MLSP), 2019.
• A soft STAPLE algorithm combined with anatomical knowledge. Eytan Kats, Jacob Goldberger and Hayit Greenspan, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019.
• Multi-microphone speaker separation based on deep DOA estimation. Shlomo E. Chazan, Hodaya Hammer, Gershon Hazan, Jacob Goldberger and Sharon Gannot, The European Signal Processing Conference (EUSIPCO), 2019.
• Precise detection in densely packed scenes. Eran Goldman, Roei Herzig, Aviv Eisenschtat, Jacob Goldberger and Tal Hassner, IEEE Computer Vision and Pattern Recognition (CVPR), 2019. data and code
• Aligning vector-spaces with noisy supervised lexicon. Noa Yehezkel Lubin, Jacob Goldberger and Yoav Goldberg, North American Chapter of the Association for Computational Linguistics (NAACL), 2019.
• Formant estimation and tracking: a deep learning approach. Yehoshua Dissen, Jacob Goldberger and Joseph Keshet, The Journal of the Acoustical Society of America, vol. 145(2), pp. 642-653, 2019.
• Network adaptation strategies for learning new classes without forgetting the original ones. Hagai Taitelbaum, Gal Chechik and Jacob Goldberger, IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2019.
• Information-bottleneck based on the Jensen-Shannon divergence with applications to pairwise clustering. Jacob Goldberger and Yaniv Opochinsky, IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2019.
• Clustering-driven deep embedding with pairwise constraints. Sharon Fogel, Hadar Averbuch-Elor, Daniel Cohen-Or and Jacob Goldberger , IEEE Computer Graphics and Applications , vol. 39(4), pp. 16-27, 2019.
• Classification and detection in mammograms with weak supervision via dual branch deep neural net. Ran Bakalo, Rami Ben-Ari and Jacob Goldberger, IEEE International Symposium on Biomedical Imaging (ISBI), 2019.
• Soft labeling by distilling anatomical knowledge for improved MS lesion segmentation. Eytan Kats, Jacob Goldberger and Hayit Greenspan, IEEE International Symposium on Biomedical Imaging (ISBI), 2019.
• A mixture of views network with applications to the classification of breast microcalcications. Yaniv Shachor, Hayit Greenspan and Jacob Goldberger IEEE International Symposium on Biomedical Imaging (ISBI), 2019.

2018

• GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Maayan Frid-Adar, Idit Diamant, Eyal Klang, Michal Amitai, Jacob Goldberger and Hayit Greenspan, Neurocomputing, vol. 321, pp. 321-331, 2018.
• Attention-based neural network for joint diarization and speaker extraction Shlomo E. Chazan, Sharon Gannot and Jacob Goldberger, Int. Workshop on Acoustic Signal Enhancement (IWAENC), 2018.
• Adding new classes without access to the original training data with applications to language identification. Hagai Taitelbaum, Ehud Ben-Reuven and Jacob Goldberger, INTERSPEECH, 2018.
• LCMV beamformer with DNN-based multichannel concurrent speakers detector. Shlomo E. Chazan, Jacob Goldberger and Sharon Gannot, European Signal Processing Conference (EUSIPCO), 2018.
• Speech dereverberation using fully convolutional networks. Ori Ernst, Shlomo E. Chazan, Sharon Gannot and Jacob Goldberger, European Signal Processing Conference (EUSIPCO), 2018.
• Self-normalization properties of language modeling. Jacob Goldberger and Oren Melamud, The International Conference on Computational Linguistics (COLING), 2018.
• Training a neural network based on unreliable human annotation of medical images. Yair Dgani, Hayit Greenspan and Jacob Goldberger, IEEE International Symposium on Biomedical Imaging (ISBI), 2018.
• Synthetic data augmentation using GAN for improved liver lesion classification. Maayan Frid-Adar, Eyal Klang, Michal Amitai, Jacob Goldberger and Hayit Greenspan, IEEE International Symposium on Biomedical Imaging (ISBI), 2018.
• Anatomical data augmentation for CNN based pixel-wise classification. Avi Ben-Cohen, Eyal Klang, Michal Amitai, Jacob Goldberger and Hayit Greenspan, IEEE International Symposium on Biomedical Imaging (ISBI), 2018.
• Training strategies for deep latent models and applications to speech presence probability estimation. Shlomo E. Chazan, Sharon Gannot and Jacob Goldberger, Int. Conference on Latent Variable Analysis and Signal Separation (LVA/ICA), 2018.
• DNN-based concurrent speaker detector and its application to speaker extraction with LCMV beamforming. Shlomo E. Chazan, Jacob Goldberger and Sharon Gannot, IEEE Int. Conference on Acoustic, Speech and Signal Processing (ICASSP), 2018

2017

• Information-theory interpretation of the skip-gram negative-sampling objective function. Oren Melamud and Jacob Goldberger, Annual Meeting of the Association for Computational Linguistics (ACL), 2017.
• A simple language model based on PMI matrix approximations. Oren Melamud, Ido Dagan and Jacob Goldberger, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2017.
• Deep recurrent mixture of experts for speech enhancement. Shlomo E. Chazan, Jacob Goldberger and Sharon Gannot, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2017.
• Successive relative transfer function identification using single microphone speech enhancement. Dany Cherkassky, Shlomo E. Chazan, Jacob Goldberger and Sharon Gannot, The European Signal Processing Conference (EUSIPCO), 2017.
• Speaker extraction using LCMV beamformer with DNN-based SPP and RTF identification scheme. Ariel Malek, Shlomi Chazan, Ilan Malka, Vladimir Tourbabin, Jacob Goldberger, Eli Tzirkel-Hancock and Sharon Gannot, The European Signal Processing Conference (EUSIPCO), 2017.
• Task driven dictionary learning based on mutual information for medical image classification. Idit Diamant, Eyal Klang, Michal Amitai, Eli Konen, Jacob Goldberger and Hayit Greenspan, IEEE Trans. on Biomedical Engineering, vol. 64(6), pp. 1380-92, 2017.
• A deep neural network with a restricted noisy channel for identification of functional introns. Alan Joseph Bekker, Michal Chorev, Liran Carmel and Jacob Goldberger, IEEE Machine Learning for Signal Processing Workshop (MLSP), 2017.
• Training deep neural-networks using a noise adaptation layer. Jacob Goldberger and Ehud Ben-Reuven, Int. Conference on Learning Representations (ICLR), 2017. code
• Atlas of classifiers for brain MRI segmentation. Boris Veselov, Shiri Gordon, Jacob Goldberger and Tammy Riklin-Raviv, MICCAI Int. Workshop on Machine Learning in Medical Imaging (MLMI), 2017.
• Modeling the intra-class variability for liver lesion detection using a multi-class patch-based CNN. Maayan Frid-Adar, Idit Diamant, Eyal Klang, Michal Amitai, Jacob Goldberger and Hayit Greenspan, MICCAI Int. Workshop on Patch-based Techniques in Medical Imaging (PatchMI), 2017.
• Identification of introns harboring functional sequence elements through positional conservation. Michal Chorev, Alan Joseph Bekker, Jacob Goldberger and Liran Carmel, Scientific Reports 7, Article number: 4201, 2017.

2016

• A multi-view deep learning architecture for classification of breast microcalcifications. Alan Joseph Bekker, Hayit Greenspan and Jacob Goldberger, IEEE International Symposium on Biomedical Imaging (ISBI), 2016.
• Pairwise clustering based on the mutual-information criterion. Amir Alush, Avishay Friedman and Jacob Goldberger, Neurocomputing vol. 182, pp. 284-293, 2016.
• Multi-view probabilistic classification of breast microcalcifications. Alan Joseph Bekker, Moran Shalhon, Hayit Greenspan and Jacob Goldberger, IEEE Trans. Medical Imaging, vol. 35:2 pp. 645-653, 2016.
• Patch-based segmentation with spatial consistency: application to MS lesions in brain MRI. Roey Mechrez, Jacob Goldberger and Hayit Greenspan, International Journal of Biomedical Imaging, Article ID 7952541, doi:10.1155/2016/7952541, 2016.
• Hierarchical image segmentation using correlation clustering. Amir Alush and Jacob Goldberger, IEEE Trans. on Neural Networks and Learning Systems, vol. 27(6), pp. 1358-1367, 2016.
• A semisupervised approach for language identification based on ladder networks. Ehud Ben-Reuven and Jacob Goldberger, Odyssey Speaker and Language Recognition Workshop, 2016.
• Training deep neural-networks based on unreliable labels. Alan Joseph Bekker and Jacob Goldberger, IEEE Int. Conf. on Acoustic, Speech and Signal Processing (ICASSP), 2016.
• Combining soft decisions of several unreliable experts. Jacob Goldberger, IEEE Int. Conf. on Acoustic, Speech and Signal Processing (ICASSP), 2016.
• A pre-training approach for deep neural network with application to speech enhancement. Shlomo E. Chazan, Sharon Gannot and Jacob Goldberger, Int. Workshop on Acoustic Signal Enhancement (IWAENC), 2016. Best student paper award
• Context2vec: learning generic context embedding with bidirectional LSTM. Oren Melamud, Jacob Goldberger and Ido Dagan, Conference on Computational Natural Language Learning (CoNLL), 2016. code
• Combining clusterings with different detail levels. Oded Kaminsky and Jacob Goldberger, IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2016.
• Intra-cluster training strategy for deep learning with applications to language identification. Alan Jospeh Bekker, Irit Opher, Itsik Lapidot and Jacob Goldberger, IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2016.
• A hybrid approach for speech enhancement using MoG model and neural network phoneme classifier. Shlomo E. Chazan, Jacob Goldberger and Sharon Gannot, IEEE Trans. on Audio, Speech and Language Processing, vol. 24(12), pp. 2516-2530, 2016.

2015

• Obstacle detection in a greenhouse environment using the Kinect sensor. Sharon Nissimov, Jacob Goldberger and Victor Alchanatis, Computers and Electronics in Agriculture, vol. 113, pp. 104-115, 2015.
• Efficient global learning of entailment graphs. Jonathan Berant, Noga Alon, Ido Dagan and Jacob Goldberger, Computational Linguistics, vol. 41:2, pp. 221–263. 2015.
• Learning to exploit structured resources for lexical inference. Vered Shwartz, Omer Levy, Ido Dagan and Jacob Goldberger, Conference on Computational Natural Language Learning (CoNLL), 2015.
• Modeling word meaning in context with substitute vectors. Oren Melamud, Ido Dagan and Jacob Goldberger, North American Chapter of the ACL (NAACL), 2015.
• Learning to combine decisions from multiple mammography views. Alan Joseph Bekker, Moran Shalhon, Hayit Greenspan and Jacob Goldberger, IEEE International Symposium on Biomedical Imaging (ISBI), 2015.
• Multi-phase liver lesions classification using relevant visual words based on mutual information. Idit Diamant, Jacob Goldberger, Eyal Klang, Michal Amitai and Hayit Greenspan, IEEE International Symposium on Biomedical Imaging (ISBI), 2015.

2014

• Probabilistic modeling of joint-context in distributional similarity. Oren Melamud, Ido Dagan, Jacob Goldberger, Idan Szpektor and Deniz Yuret, Conference on Computational Natural Language Learning (CoNLL) , 2014. Best paper runner up.
• Focused entailment graphs for open IE propositions. Omer Levy, Ido Dagan and Jacob Goldberger, Conference on Computational Natural Language Learning (CoNLL) , 2014.
• MIMO detection based on averaging Gaussian projections. Jacob Goldberger, IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2014.

2013

• A two level model for context sensitive inference rules. Oren Melamud, Jonathan Berant, Ido Dagan, Jacob Goldberger and Idan Szpektor, Annual Meeting of the Association for Computational Linguistics (ACL), 2013. Best paper runner up.
• Using lexical expansion to learn inference rules from sparse data. Oren Melamud, Ido Dagan, Jacob Goldberger and Idan Szpektor, Annual Meeting of the Association for Computational Linguistics (ACL), short paper, 2013.
• Improved MIMO detection based on successive tree approximations. Jacob Goldberger, IEEE Int. Symposium on Information Theory (ISIT), 2013. C code
• Break and conquer: efficient correlation clustering for image segmentation. Amir Alush and Jacob Goldberger, Int. Workshop on Similarity-Based Pattern Analysis and Recognition (SIMBAD), 2013.
• Information theoretic pairwise clustering. Avishay Friedman and Jacob Goldberger, Int. Workshop on Similarity-Based Pattern Analysis and Recognition (SIMBAD) , 2013.
• Breast tissue classification in mammograms using visual words, Idit Diamant, Hayit Greenspan and Jacob Goldberger, SPIE Medical Imaging, 2013.

2012

• Ensemble segmentation using efficient integer linear programming. Amir Alush and Jacob Goldberger, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 34:10, pp. 1966-1977, 2012.
• Efficient tree-based approximation for entailment graph learning. Jonathan Berant, Ido Dagan, Meni Adler and Jacob Goldberger, Annual Meeting of the Association for Computational Linguistics (ACL), 2012.
• A Probabilistic Lexical Model for Ranking Textual Inferences. Eyal Shnarch, Ido Dagan, Jacob Goldberger, *SEM (Joint Conference on Lexical and Computational Semantics), 2012.
• Unsupervised Feature Selection based on Non-Parametric Mutual Information. Lev Faivishevsky and Jacob Goldberger, IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2012.
• Dimensionality reduction based on non-parametric mutual information. Lev Faivishevsky and Jacob Goldberger, Neurocomputing, vol. 80, pp. 31-37, 2012.
• Learning entailment relations by global graph structure optimization. Jonathan Berant, Ido Dagan and Jacob Goldberger, Computational Linguistics, vol. 38:1, pp. 1-39, 2012.
• Beyond Condorcet: Optimal aggregation rules using voting records. Eyal Baharad, Jacob Goldberger, Moshe Koppel and Shmuel Nitzan, Theory and Decision, vol. 72, pp. 113-130, 2012.
• An unsupervised data projection that preserves the cluster structure. Lev Faivishevsky and Jacob Goldberger, Pattern Recognition Letters , vol. 33, pp. 256-262, 2012.

2011

• Iterative tomographic solution of integer least squares problems with applications to MIMO detection.  Jacob Goldberger and Amir Leshem, IEEE Journal of Selected Topics in Signal Processing, vol. 5, pp. 1486-1496, 2011.
• MIMO detection for high-order QAM based on a Gaussian tree approximation. Jacob Goldberger and Amir Leshem, IEEE Trans. Information Theory , vol. 57, pp. 4973-82, 2011. C code
• X-ray categorization and spatial localization of chest pathologies.  Uri Avni, Hayit Greenspan and Jacob Goldberger, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2011.
• Towards a probabilistic model for lexical entailment.  Eyal Shnarch, Jacob Goldberger and Ido Dagan, TextInfer-Workshop on Textual Entailment, 2011.
• Global learning of typed entailment rules. Jonathan Berant, Ido Dagan and Jacob Goldberger, Annual Meeting of the Association for Computational Linguistics (ACL), 2011. Received the best student long paper award.
• A probabilistic modeling framework for lexical entailment. Eyal Shnarch, Jacob Goldberger and Ido Dagan, Annual Meeting of the Association for Computational Linguistics ACL), 2011.
• X-ray categorization and retrieval on the organ and pathology level, using patch-based visual words. Uri Avni, Hayit Greenspan, Eli Konen, Michal Sharon, and Jacob Goldberger, IEEE Trans. Medical Imaging , vol. 30, pp. 733-746, 2011.
• Distilling the wisdom of crowds: Weighted aggregation of decisions on multiple issues. Eyal Baharad, Jacob Goldberger, Moshe Koppel and Shmuel Nitzan, Autonomous Agents and Multi-Agents Systems, vol. 22, pp. 31-42, 2011.

2010

• Mutual information based dimensionality reduction with application to non-linear regression. Lev Faivishevsky and Jacob Goldberger, IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2010.
• Global learning of focused entailment graphs. Jonathan Berant, Ido Dagan and Jacob Goldberger, Annual Meeting of the Association for Computational Linguistics (ACL), 2010.
• A nonparametric information theoretic clustering algorithm.  Lev Faivishevsky and Jacob Goldberger, International Conference Machine Learning (ICML), 2010.
• Efficient anonymizations with enhanced utility.  Jacob Goldberger and Tamir Tassa, Transactions on Data Privacy, pp. 149-175, 2010.
• Automated and interactive lesion detection and segmentation in uterine cervix Images. Amir Alush, Hayit Greenspan and Jacob Goldberger, IEEE Trans. Medical Imaging, vol. 29, pp. 488-501, 2010.
• Pseudo prior belief propagation for densely connected discrete graphs. Jacob Goldberger and Amir Leshem, IEEE Information Theory Workshop (ITW), 2010.

2009

• Analyzing movement trajectories using a Markov bi-clustering method. Keren Erez, Jacob Goldberger, Ronen Sosnik, Moshe Shemesh, Susan Rothstein, and Moshe Abeles, Journal of Computational Neuroscience, vol. 27, pp. 543-552, 2009.
• Classification of hyperspectral remote-sensing images using discriminative linear projections. Lior Weizman and Jacob Goldberger, International Journal of Remote Sensing, vol. 30, Issue 21, pp. 5605-17, 2009.
• Multiple sclerosis lesion detection using constrained GMM and curve evolution. Oren Freifeld, Hayit Greenspan and Jacob Goldberger, International Journal of Biomedical Imaging, doi:10.1155/2009/715124, 2009.
• A Gaussian tree approximation for integer least-squares. Jacob Goldberger and Amir Leshem, Neural Information Processing Systems (NIPS), 2009.
• Efficient anonymizations with enhanced utility. Jacob Goldberger and Tamir Tassa, International Workshop on Privacy Aspects of Data Mining (PADM), Miami, 2009.
• Urban area segmentation using visual words. Lior Weizman and Jacob Goldberger, IEEE Geoscience and Remote Sensing Letters, vol. 6, no. 3, pp. 388-392, 2009.
• Lesion detection and segmentation in uterine cervix images using an arc-level MRF.  Amir Alush, Hayit Greenspan and Jacob Goldberger, IEEE International Symposium on Biomedical Imaging (ISBI), 2009.
• X-ray image categorization and retrieval using patch-based visual words representation. Uri Avni, Hayit Greenspan, Michal Sharon, Eli Konen and Jacob Goldberger, IEEE International Symposium on Biomedical Imaging (ISBI), 2009.
• Addressing the ImageClef 2009 Challenge using a patch-based visual words.  Uri Avni, Jacob Goldberger and Hayit Greenspan, The Cross Language Evaluation Forum Workshop (CLEF), 2009. Winner of the CLEF medical image annotation challenge.
• MIMO decoding based on stochastic reconstruction from multiple projections.  Amir Leshem and Jacob Goldberger, IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2009.

2008

• Simplifying mixture models using the unscented transform. Jacob Goldberger, Hayit Greenspan and Jeremie Dreyfuss, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, pp. 1496-1502, 2008.
• Hierarchical clustering algorithm based on the Hungarian method. Jacob Goldberger and Tamir Tassa, Pattern Recognition Letters 29, pp. 1632-38, 2008.
• Face recognition using classification based linear projections.  Moshe Butman and Jacob Goldberger, EURASIP Journal on Advances in Signal Processing, vol. 8, Issue 2, 2008.
• Automatic acoustic detection of the red palm weevil.  Joel Pinhas, Victoria Soroker, Amots Hetzroni, Amos Mizrach, Mina Teicher and Jacob Goldberger, Computers and Electronics in Agriculture, vol. 63, pages 131-139, 2008.
• Serial schedules for belief-propagation: analysis of convergence time. Jacob Goldberger and Haggai Kfir, IEEE Trans. on Information Theory, pp. 1316-19, 2008.
• Unifying unknown nodes in the internet graph using semisupervised spectral clustering. Anat Almog, Jacob Goldberger and Yuval Shavitt, The International Workshop on Mining Complex Data (MCD), 2008.
• ICA based on a smooth estimation of the differential entropy. Lev Faivishevsky and Jacob Goldberger, Neural Information Processing Systems (NIPS), 2008.
• Detection of urban zones in satellite images using visual words. Lior Weizman and Jacob Goldberger, SPIE Int. Geoscience and Remote Sensing Symposium (IGARSS), 2008.
• Contextual preferences. Idan Szpektor, Ido Dagan, Roy Bar-Haim and Jacob Goldberger, Annual Meeting of the Association for Computational Linguistics (ACL), 2008.

2007

• Efficient serial message-passing schedules for LDPC decoding. Eran Sharon, Simon Litsyn and Jacob Goldberger, IEEE Trans. on Information Theory, pp. 4076-91, 2007.
• Fast semi-supervised discriminative component analysis. Jaakko Peltonen, Jacob Goldberger, and Samuel Kaski, IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2007.
• A Markov clustering method for analyzing movement trajectories. Jacob Goldberger, Keren Erez and Moshe Abeles, IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2007.
• A classification based linear projection of labeled Hyperspectral data. Lior Weizman and Jacob Goldberger, SPIE Int. Geoscience and Remote Sensing Symposium (IGARSS), 2007.
• Combining region and edge cues for image segmentation in a probabilistic Gaussian mixture framework. Omer Rotem, Hayit Greenspan and Jacob Goldberger, Computer Vision and Pattern Recognition (CVPR), 2007.
• An optimal reduced representation of a MoG with applications to medical image database classification. Jacob Goldberger, Hayit Greenspan and Jeremie Dreyfuss, Computer Vision and Pattern Recognition (CVPR), 2007.
• Lesion detection in noisy MR brain images using constrained GMM and active contours. Oren Freifeld, Hayit Greenspan and Jacob Goldberger, International Symposium on Biomedical Imaging (ISBI), 2007.

2006

• Constrained Gaussian mixture model framework for automatic segmentation of MR brain images. Hayit Greenspan, Amit Ruf and Jacob Goldberger, IEEE Trans. on Medical Imaging, pp. 1233-45, 2006.
• An information theoretic framework for unsupervised image clustering. Jacob Goldberger, Shiri Gordon and Hayit Greenspan, IEEE Trans. on Image Processing, vol. 15, pp. 449-458, 2006.
• Context-based segmentation of image sequences. Jacob Goldberger and Hayit Greenspan, IEEE Pattern Analysis and Machine Intelligence, vol. 28, pp. 463-468, 2006.
• Convergence analysis of message-passing schedules for LDPC decoding. Eran Sharon, Simon Litsyn and Jacob Goldberger, The 4th International Symposium on Turbo Codes, 2006.

2005

• Projective reconstruction from pairwise overlapping multiple views. Jacob Goldberger, Journal of Computer Vision and Image Understanding, pp. 283-296, 2005.
• Tissue classification of noisy MR brain images using constrained GMM. Amit Ruf, Hayit Greenspan and Jacob Goldberger, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2005.
• A distance measure between GMMs based on the unscented transform and its application to speaker recognition. Jacob Goldberger and Hagai Aronowitz, Eurospeech, 2005.

2004

• Probabilistic space-time video modeling via piecewise GMM. Hayit Greenspan, Jacob Goldberger and Arnaldo Mayer, IEEE Pattern Analysis and Machine Intelligence, vol. 26, pp. 384-406, 2004.
• Neighbourhood Component Analysis. Jacob Goldberger, Sam Roweis, Geoff Hinton and Ruslan Salakhutdinov, Neural Information Processing Systems (NIPS), 2004.
  matlab code C code
• Hierarchical clustering of a mixture model. Jacob Goldberger and Sam Roweis, Neural Information Processing Systems (NIPS), 2004.
• An efficient message-passing schedule for LDPC decoding. Eran Sharon, Simon Litsyn and Jacob Goldberger, Proc. Electrical and Electronic Engineers in Israel, 2004.

2003

• An efficient similarity measure based on approximations of KL-divergence between two Gaussian mixtures. Jacob Goldberger, Hayit Greenspan and Shiri Gordon, International Conference on Computer Vision (ICCV), 2003.
• Applying the information bottleneck principle to unsupervised clustering of discrete and continuous image representations. Shiri Gordon, Jacob Goldberger and Hayit Greenspan, International Conference on Computer Vision (ICCV), 2003.

2002

• A probabilistic framework for spatio-temporal video representation and indexing.  Hayit Greenspan, Jacob Goldberger and Arnaldo Mayer, European Conference on Computer Vision (ECCV) , 2002.
• Probabilistic models for generating, modelling and matching image categories. Hayit Greenspan, Shiri Gordon and Jacob Goldberger, International Conference on Pattern Recognition (ICPR), 2002.
• Unsupervised image clustering using the information bottleneck method. Jacob Goldberger, Hayit Greenspan and Shiri Gordon, The Annual Pattern Recognition Conference DAGM, Zurich, 2002.

2001

• A continuous probabilistic framework for image matching. Hayit Greenspan, Jacob Goldberger and Lenny Riddel, Journal of Computer Vision and Image Understanding 84, pp. 384-406, 2001.
• Mixture model for face color modelling and segmentation.  Hayit Greenspan, Jacob Goldberger and Itay Eshet, Pattern Recognition Letters 22, pp. 1525-1536, 2001.
• Sequentially finding the N-best list in hidden Markov models.  Dennis Nilsson and Jacob Goldberger, International Conference on Artificial Intelligence (IJCAI), 2001.

1997-99

• Segmental modeling using a continuous mixture of non-parametric models. Jacob Goldberger, David Burshtein and Horacio Franco, IEEE Trans. Speech Audio Proc, vol. 7, pp. 262-271, 1999.
• Registration of multiple point sets using the EM algorithm. Jacob Goldberger, International Conference on Computer Vision (ICCV), 1999.
• Scaled random trajectory segmental models. Jacob Goldberger and David Burshtein, Computer Speech and Language 12, pp. 51-73, 1998.
• Scaled random trajectory segmental models.  Jacob Goldberger and David Burshtein, IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 1998.
• Segmental modelling using a continuous mixture of non-parametric models. Jacob Goldberger, David Burshtein and Horacio Franco, EuroSpeech, 1997.

Ph.D. Thesis

• Segmental modelling using a continuous mixture of random trajectory segmental models for improved automatic speech recognition. Jacob Goldberger, 1998.

Technical Reports

• Solving Sudoku using combined message passing algorithms. Jacob Goldberger, Technical Reort TR-BIU-ENG-2007-05-03, Engineering School, Bar-Ilan Univ., 2007.

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Publications by topic by year

Methods [graphical models][clustering][dimension reduction][classification][mixture models][information theory in machine learning][calibration][domain adaptation]
Applications [computer vision][medical imaging][remote sensing][language processing][speech processing][wireless communication][error correcting codes]

confidence calibration and conformal prediction

• Noise-robust conformal prediction for medical image classification, Coby Penso and Jacob Goldberger, MICCAI Int. Workshop on Machine Learning in Medical Imaging (MLMI), 2024.
• Calibration of network confidence for unsupervised domain adaptation using estimated accuracy,  The ECCV workshop on Uncertainty Quantification for Computer Vision, Coby Penso and Jacob Goldberger, 2024.
• A conformalized learning of a prediction set with applications to medical imaging classification, Roy Hirsch and Jacob Goldberger, IEEE International Symposium on Biomedical Imaging (ISBI), 2024.
• A joint training and confidence calibration procedure that is robust to label noise, Coby Penso and Jacob Goldberger,  IEEE International Symposium on Biomedical Imaging (ISBI), 2024.
• Confidence calibration of a medical imaging classification system that is robust to label noise, Coby Penso, Lior Frenkel and Jacob Goldberger, IEEE Transactions on Medical Imaging, vol. 43(6), pp.  2050-2060,  2024.
• Conformal nucleus sampling,  Shauli Ravfogel, Yoav Goldberg and Jacob Goldberger, Findings of ACL, 2023.
• Calibration of a regression network based on the predictive variance with applications to medical images, Lior Frenkel and Jacob Goldberger, IEEE International Symposium on Biomedical Imaging (ISBI), 2023.
• Network calibration by temperature scaling based on the predicted confidence, Lior Frenkel and Jacob Goldberger, The European Signal Processing Conference (EUSIPCO), 2022.
• Calibration of medical imaging classification systems with weight scaling, Lior Frenkel and Jacob Goldberger, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022.
• Network calibration by class-based temperature scaling, Lior Frenkel and Jacob Goldberger, The European Signal Processing Conference (EUSIPCO), 2021.

domain adaptation

• De-confusing pseudo-labels in source-free domain adaptation, Idit Diamant, Amir Rosenfeld, Idan Achituve, Jacob Goldberger, Arnon Netzer,  European Conference on Computer Vision (ECCV), 2024.
• Domain adaptation using suitable pseudo labels for speech enhancement and dereverberation, Lior Frenkel, Shlomi Chazan and Jacob Goldberger, IEEE Transactions on Audio, Speech and Language Processing,  vol.  36. pp.  1226-1236, 2024.
• Domain adaptation for speech enhancement in a large domain gap, Lior Frenkel, Jacob Goldberger and  Shlomo Chazan,  Interspeech, 2023.
• PLST: A pseudo-labels with a smooth transition Strategy for medical site adaptation, Tomer Bar Natan, Hayit Greenspan and Jacob Goldberger,  MICCAI  Workshop on Domain Adaptation and Representation Transfer (DART), 2023.
• PLPP: A pseudo labeling post-processing strategy for unsupervised domain adaptation,  Tomer Bar Natan, Hayit Greenspan and Jacob Goldberger, IEEE International Symposium on Biomedical Imaging (ISBI), 2023.
• Supervised domain adaptation using gradient transfer for improved medical image analysis, Shaya Goodman, Hayit Greenspan and Jacob Goldberger,  MICCAI  Workshop on Domain Adaptation and Representation Transfer (DART), 2022.
• Unsupervised site adaptation by intra-site variability alignment,  Shaya Goodman*, Shira Kasten Serlin*, Hayit Greenspan and Jacob Goldberger,  MICCAI  Workshop on Domain Adaptation and Representation Transfer (DART), 2022.
• Adaptation of a multisite network to a new clinical site via batch-normalization similarity, Shira Kasten Serlin, Jacob Goldberger and Hayit Greenspan, IEEE International Symposium on Biomedical Imaging (ISBI), 2022.
• Transfer learning with a layer dependent regularization for medical image segmentation, Nimrod Sagie, Hayit Greenspan and Jacob Goldberger, MICCAI Int. Workshop on Machine Learning in Medical Imaging (MLMI), 2021.
• Transfer learning via parameter regularization for medical image segmentation, Nimrod Sagie, Hayit Greenspan and Jacob Goldberger, The European Signal Processing Conference (EUSIPCO), 2021.

computer vision

• CRF with deep class embedding for large scale classification. Eran Goldman and Jacob Goldberger, Computer Vision and Image Understanding (CVIU), vol. 191, pp. 1-9, 2020.
• Precise detection in densely packed scenes. Eran Goldman, Roei Herzig, Aviv Eisenschtat, Jacob Goldberger and Tal Hassner, IEEE Computer Vision and Pattern Recognition (CVPR), 2019. data and code
• Combining clusterings with different detail levels. Oded Kaminsky and Jacob Goldberger, IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2016.
• Hierarchical image segmentation using correlation clustering. Amir Alush and Jacob Goldberger, IEEE Trans. on Neural Networks and Learning Systems, vol. 27(6), pp. 1358-1367, 2016.
• Obstacle detection in a greenhouse environment using the Kinect sensor. Sharon Nissimov, Jacob Goldberger and Victor Alchanatis, Computers and Electronics in Agriculture, vol. 113, pp. 104-115, 2015.
• Break and conquer: efficient correlation clustering for image segmentation. Amir Alush and Jacob Goldberger, Int. Workshop on Similarity-Based Pattern Analysis and Recognition (SIMBAD), 2013.
• Ensemble segmentation using efficient integer linear programming. Amir Alush and Jacob Goldberger, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 34:10, pp. 1966-1977, 2012.
• Face recognition using classification based linear projections. Moshe Butman and Jacob Goldberger, EURASIP Journal on Advances in Signal Processing, vol. 8, Issue 2, 2008.
• Combining region and edge cues for image segmentation in a probabilistic Gaussian mixture framework. Omer Rotem, Hayit Greenspan and Jacob Goldberger. Computer Vision and Pattern Recognition (CVPR), 2007.
• An information theoretic framework for unsupervised image clustering. Jacob Goldberger, Shiri Gordon and Hayit Greenspan, IEEE Trans. on Image Processing, vol. 15, pp. 449-458, 2006.
• Context-based segmentation of image sequences. Jacob Goldberger and Hayit Greenspan, IEEE Pattern Analysis and Machine Intelligence, vol. 28, pp. 463-468, 2006.
• Projective reconstruction from pairwise overlapping multiple views. Jacob Goldberger, Journal of Computer Vision and Image Understanding, pp. 283-296, 2005.
• Probabilistic space-time video modeling via piecewise GMM. Hayit Greenspan, Jacob Goldberger and Arnaldo Mayer.IEEE Pattern Analysis and Machine Intelligence, vol. 26, pp. 384-406, 2004.
• An efficient similarity measure based on approximations of KL-divergence between two Gaussian mixtures.Jacob Goldberger, Hayit Greenspan and Shiri Gordon, International Conference on Computer Vision (ICCV), 2003.
• Applying the information bottleneck principle to unsupervised clustering of discrete and continuous image representations. Shiri Gordon, Jacob Goldberger and Hayit Greenspan, International Conference on Computer Vision (ICCV), 2003.
• A probabilistic framework for spatio-temporal video representation and indexing. Hayit Greenspan, Jacob Goldberger and Arnaldo Mayer, European Conference on Computer Vision (ECCV) , 2002.
• Probabilistic models for generating, modelling and matching image categories. Hayit Greenspan, Shiri Gordon and Jacob Goldberger, International Conference on Pattern Recognition (ICPR), 2002.
• Unsupervised image clustering using the information bottleneck method. Jacob Goldberger, Hayit Greenspan and Shiri Gordon, The Annual Pattern Recognition Conference DAGM, Zurich, 2002.
• A continuous probabilistic framework for image matching. Hayit Greenspan, Jacob Goldberger and Lenny Riddel, Journal of Computer Vision and Image Understanding 84, pp. 384-406, 2001.
• Mixture model for face color modelling and segmentation. Hayit Greenspan, Jacob Goldberger and Itay Eshet, Pattern Recognition Letters 22, pp. 1525-1536, 2001.
• Registration of multiple point sets using the EM algorithm. Jacob Goldberger, International Conference on Computer Vision (ICCV), 1999.

medical imaging

• Confidence calibration of a medical imaging classification system that is robust to label noise, Coby Penso, Lior Frenkel and Jacob Goldberger, IEEE Transactions on Medical Imaging, vol. 43(6), pp.  2050-2060,  2024.
• PLPP: A pseudo labeling post-processing strategy for unsupervised domain adaptation,  Tomer Bar Natan, Hayit Greenspan and Jacob Goldberger, IEEE International Symposium on Biomedical Imaging (ISBI), 2023.
• PLST: A pseudo-labels with a smooth transition Strategy for medical site adaptation, Tomer Bar Natan, Hayit Greenspan and Jacob Goldberger,  MICCAI  Workshop on Domain Adaptation and Representation Transfer (DART), 2023.
• Calibration of medical imaging classification systems with weight scaling, Lior Frenkel and Jacob Goldberger, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022.
• Supervised domain adaptation using gradient transfer for improved medical image analysis, Shaya Goodman, Hayit Greenspan and Jacob Goldberger,  MICCAI  Workshop on Domain Adaptation and Representation Transfer (DART), 2022.
• Unsupervised site adaptation by intra-site variability alignment,  Shaya Goodman*, Shira Kasten Serlin*, Hayit Greenspan and Jacob Goldberger,  MICCAI  Workshop on Domain Adaptation and Representation Transfer (DART), 2022.
• Calibration of medical imaging classification systems with weight scaling, Lior Frenkel and Jacob Goldberger, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022.
• Class-based attention mechanism for chest radiograph multi-label categorization, David Sriker, Hayit Greenspan and Jacob Goldberger, IEEE International Symposium on Biomedical Imaging (ISBI), 2022.
• Adaptation of a multisite network to a new clinical site via batch-normalization similarity, Shira Kasten Serlin, Jacob Goldberger and Hayit Greenspan, IEEE International Symposium on Biomedical Imaging (ISBI), 2022.
• An atlas of classifiers - A machine learning paradigm for brain MRI Segmentation, Shiri Gordon, Boris Kodner, Tal Goldfryd, Michael Sidorov, Jacob Goldberger, Tammy Riklin-Raviv,  Medical, Biological Eng & Computing (MBEC), 2021.
• Transfer learning with a layer dependent regularization for medical image segmentation, Nimrod Sagie, Hayit Greenspan and Jacob Goldberger, MICCAI Int. Workshop on Machine Learning in Medical Imaging (MLMI), 2021.
• Transfer learning via parameter regularization for medical image segmentation, Nimrod Sagie, Hayit Greenspan and Jacob Goldberger, The European Signal Processing Conference (EUSIPCO), 2021.
• Learning probabilistic fusion of multilabel lesion contours. Gal Cohen, Hayit Greenspan and Jacob Goldberger, IEEE International Symposium on Biomedical Imaging (ISBI), 2020.
• A soft STAPLE algorithm combined with anatomical knowledge. Eytan Kats, Jacob Goldberger and Hayit Greenspan, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019.
• Classification and detection in mammograms with weak supervision via dual branch deep neural net. Ran Bakalo, Rami Ben-Ari and Jacob Goldberger, IEEE International Symposium on Biomedical Imaging (ISBI), 2019.
• Soft labeling by distilling anatomical knowledge for improved MS lesion segmentation. Eytan Kats, Jacob Goldberger and Hayit Greenspan, IEEE International Symposium on Biomedical Imaging (ISBI), 2019.
• A mixture of views network with applications to the classification of breast microcalcications. Yaniv Shachor, Hayit Greenspan and Jacob Goldberger IEEE International Symposium on Biomedical Imaging (ISBI), 2019.
• Training a neural network based on unreliable human annotation of medical images. Yair Dgani, Hayit Greenspan and Jacob Goldberger, IEEE International Symposium on Biomedical Imaging (ISBI), 2018.
• Synthetic data augmentation using GAN for improved liver lesion classification. Maayan Frid-Adar, Eyal Klang, Michal Amitai, Jacob Goldberger and Hayit Greenspan, IEEE International Symposium on Biomedical Imaging (ISBI), 2018.
• Anatomical data augmentation for CNN based pixel-wise classification. Avi Ben-Cohen, Eyal Klang, Michal Amitai, Jacob Goldberger and Hayit Greenspan, IEEE International Symposium on Biomedical Imaging (ISBI), 2018.
• Task driven dictionary learning based on mutual information for medical image classification. Idit Diamant, Eyal Klang, Michal Amitai, Eli Konen, Jacob Goldberger and Hayit Greenspan, IEEE Trans. on Biomedical Engineering, vol. 64(6), pp. 1380-92, 2017.
• Atlas of classifiers for brain MRI segmentation. Boris Veselov, Shiri Gordon, Jacob Goldberger and Tammy Riklin-Raviv, MICCAI Int. Workshop on Machine Learning in Medical Imaging (MLMI) , 2017.
• Modeling the intra-class variability for liver lesion detection using a multi-class patch-based CNN. Maayan Frid-Adar, Idit Diamant, Eyal Klang, Michal Amitai, Jacob Goldberger and Hayit Greenspan, MICCAI Int. Workshop on Patch-based Techniques in Medical Imaging (PatchMI), 2017.
• Multi-view probabilistic classification of breast microcalcifications. Alan Joseph Bekker, Moran Shalhon, Hayit Greenspan and Jacob Goldberger, IEEE Trans. Medical Imaging, vol. 35:2 pp. 645-653, 2016.
• Patch-based segmentation with spatial consistency: application to MS lesions in brain MRI. Roey Mechrez, Jacob Goldberger and Hayit Greenspan, International Journal of Biomedical Imaging, Article ID 7952541, doi:10.1155/2016/7952541, 2016.
• A multi-view deep learning architecture for classification of breast microcalcifications. Alan Joseph Bekker, Hayit Greenspan and Jacob Goldberger, IEEE International Symposium on Biomedical Imaging (ISBI), 2016.
• Learning to combine decisions from multiple mammography views. Alan Joseph Bekker, Moran Shalhon, Hayit Greenspan and Jacob Goldberger, IEEE International Symposium on Biomedical Imaging (ISBI), 2015.
• Multi-phase lever lesions classification using relevant visual words based on mutual information. Idit Diamant, Jacob Goldberger, Eyal Klang, Michal Amitai and Hayit Greenspan, IEEE International Symposium on Biomedical Imaging (ISBI), 2015.
• X-ray categorization and spatial localization of chest pathologies. Uri Avni, Hayit Greenspan and Jacob Goldberger. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2011.
• X-ray categorization and retrieval on the organ and pathology level, using patch-based visual words. Uri Avni, Hayit Greenspan, Eli Konen, Michal Sharon, and Jacob Goldberger, IEEE Trans. Medical Imaging , vol. 30, pp. 733-746, 2011.
• Automated and interactive lesion detection and segmentation in uterine cervix Images. Amir Alush, Hayit Greenspan and Jacob Goldberger. IEEE Trans. Medical Imaging, vol. 29, pp. 488-501, 2010.
• Multiple sclerosis lesion detection using constrained GMM and curve evolution. Oren Freifeld, Hayit Greenspan and Jacob Goldberger. International Journal of Biomedical Imaging, doi:10.1155/2009/715124, 2009.
• Lesion detection and segmentation in uterine cervix images using an arc-level MRF. Amir Alush, Hayit Greenspan and Jacob Goldberger. IEEE International Symposium on Biomedical Imaging (ISBI), 2009.
• X-ray image categorization and retrieval using patch-based visual words representation. Uri Avni, Hayit Greenspan, Michal Sharon, Eli Konen and Jacob Goldberger. IEEE International Symposium on Biomedical Imaging (ISBI), 2009.
• An optimal reduced representation of a MoG with applications to medical image database classification. Jacob Goldberger, Hayit Greenspan and Jeremie Dreyfuss. Computer Vision and Pattern Recognition (CVPR), 2007.
• Lesion detection in noisy MR brain images using constrained GMM and active contours. Oren Freifeld, Hayit Greenspan and Jacob Goldberger. International Symposium on Biomedical Imaging (ISBI), 2007.
• Constrained Gaussian mixture model framework for automatic segmentation of MR brain images. Hayit Greenspan, Amit Ruf and Jacob Goldberger, IEEE Trans. on Medical Imaging, pp. 1233-45, 2006.
• Tissue classification of noisy MR brain images using constrained GMM. Amit Ruf, Hayit Greenspan and Jacob Goldberger, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2005.

dimension reduction

• Information-theory interpretation of the skip-gram negative-sampling objective function. Oren Melamud and Jacob Goldberger, Annual Meeting of the Association for Computational Linguistics (ACL), 2017.
• Dimensionality reduction based on non-parametric mutual information. Lev Faivishevsky and Jacob Goldberger. Neurocomputing, vol. 80, pp. 31-37, 2012.
• Mutual information based dimensionality reduction with application to non-linear regression. Lev Faivishevsky and Jacob Goldberger. IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2010.
• Fast semi-supervised discriminative component analysis. Jaakko Peltonen, Jacob Goldberger, and Samuel Kaski. IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2007.
• Neighbourhood Component Analysis. Jacob Goldberger, Sam Roweis, Geoff Hinton and Ruslan Salakhutdinov, Neural Information Processing Systems (NIPS), 2004. matlab code C code

mixture models

• Simplifying mixture models using the unscented transform. Jacob Goldberger, Hayit Greenspan and Jeremie Dreyfuss, IEEE Pattern Analysis and Machine Intelligence, vol. 30, pp. 1496-1502, 2008.
• A distance measure between GMMs based on the unscented transform and its application to speaker recognition. Jacob Goldberger and Hagai Aronowitz, Eurospeech, 2005.
• Hierarchical clustering of a mixture model. Jacob Goldberger and Sam Roweis. Neural Information Processing Systems (NIPS), 2004.
• An efficient similarity measure based on approximations of KL-divergence between two Gaussian mixtures. Jacob Goldberger, Hayit Greenspan and Shiri Gordon. International Conference on Computer Vision (ICCV), 2003.

clustering

• An entangled mixture of variational autoencoders approach to deep clustering, Avi Caciularu and Jacob Goldberger, Neurocomputing,  vol. 520,  pp. 182-189,  2023.
• K-Autoencoders deep clustering. Yaniv Opochinsky, Shlomo E. Chazan, Sharon Gannot and Jacob Goldberger, IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2020.
• Deep clustering based on a mixture of autoencoders. Shlomo E. Chazan, Sharon Gannot and Jacob Goldberger, IEEE Machine Learning for Signal Processing Workshop (MLSP), 2019.
• Clustering-driven deep embedding with pairwise constraints. Sharon Fogel, Hadar Averbuch-Elor, Daniel Cohen-Or and Jacob Goldberger , IEEE Computer Graphics and Applications , vol. 39(4), pp. 16-27, 2019.
• Information-bottleneck based on the Jensen-Shannon divergence with applications to pairwise clustering. Jacob Goldberger and Yaniv Opochinsky, IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2019.
• Combining clusterings with different detail levels. Oded Kaminsky and Jacob Goldberger, IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2016.
• Pairwise clustering based on the mutual-information criterion. Amir Alush, Avishay Friedman and Jacob Goldberger, Neurocomputing vol. 182, pp. 284-293, 2016.
• Information theoretic pairwise clustering. Avishay Friedman and Jacob Goldberger, Int. Workshop on Similarity-Based Pattern Analysis and Recognition (SIMBAD) , 2013.
• A nonparametric information theoretic clustering algorithm. Lev Faivishevsky and Jacob Goldberger. International Conference Machine Learning (ICML), 2010.
• Efficient anonymizations with enhanced utility. Jacob Goldberger and Tamir Tassa. International Workshop on Privacy Aspects of Data Mining (PADM), Miami, 2009.
• Analyzing movement trajectories using a Markov bi-clustering method. Keren Erez, Jacob Goldberger, Ronen Sosnik, Moshe Shemesh, Susan Rothstein, and Moshe Abeles. Journal of Computational Neuroscience, vol. 27, pp. 543-552, 2009.
• Simplifying mixture models using the unscented transform. Jacob Goldberger, Hayit Greenspan and Jeremie Dreyfuss, IEEE Pattern Analysis and Machine Intelligence, vol. 30, pp. 1496-1502, 2008.
• Hierarchical clustering algorithm based on the Hungarian method. Jacob Goldberger and Tamir Tassa, Pattern Recognition Letters 29, pp. 1632-38, 2008.
• Unifying unknown nodels in the internet graph using semisupervised spectral clustering. Anat Almog, Jacob Goldberger and Yuval Shavitt. The International Workshop on Mining Complex Data (MCD), 2008.
• ICA based on a smooth estimation of the differential entropy. Lev Faivishevsky and Jacob Goldberger. Neural Information Processing Systems (NIPS), 2008.
• A Markov clustering method for analyzing movement trajectories. Jacob Goldberger, Keren Erez and Moshe Abeles. IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2007.
• Hierarchical clustering of a mixture model. Jacob Goldberger and Sam Roweis. Neural Information Processing Systems (NIPS), 2004.
• Applying the information bottleneck principle to unsupervised clustering of discrete and continuous image representations. Shiri Gordon, Jacob Goldberger and Hayit Greenspan, International Conference on Computer Vision (ICCV), 2003.

classification

• Network adaptation strategies for learning new classes without forgetting the original ones. Hagai Taitelbaum, Gal Chechik and Jacob Goldberger, IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2019.
• Adding new classes without access to the original training data with applications to language identification. Hagai Taitelbaum, Ehud Ben-Reuven and Jacob Goldberger, INTERSPEECH, 2018.
• Training deep neural-networks using a noise adaptation layer. Jacob Goldberger and Ehud Ben-Reuven, Int. Conference on Learning Representations (ICLR), 2017. code
• Training deep neural-networks based on unreliable labels. Alan Joseph Bekker and Jacob Goldberger, IEEE Int. Conf. on Acoustic, Speech and Signal Processing (ICASSP), 2016.
• Combining soft decisions of several unreliable experts. Jacob Goldberger, IEEE Int. Conf. on Acoustic, Speech and Signal Processing (ICASSP), 2016.

remote sensing

• Classification of hyperspectral remote-sensing images using discriminative linear projections. Lior Weizman and Jacob Goldberger. International Journal of Remote Sensing, vol. 30, Issue 21, pages 5605-17, 2009
• Urban area segmentation using visual words. Lior Weizman and Jacob Goldberger. IEEE Geoscience and Remote Sensing Letters, vol. 6, no. 3, pp. 388-392, 2009.
• Detection of urban zones in satellite images using visual words. Lior Weizman and Jacob Goldberger. SPIE Int. Geoscience and Remote Sensing Symposium (IGARSS), 2008.
• A classification based linear projection of labeled Hyperspectral data. Lior Weizman and Jacob Goldberger. SPIE Int. Geoscience and Remote Sensing Symposium (IGARSS), 2007.

graphical models

• CRF with deep class embedding for large scale classification. Eran Goldman and Jacob Goldberger, Computer Vision and Image Understanding (CVIU), vol. 191, pp. 1-9, 2020.
• Iterative tomographic solution of integer least squares problems with applications to MIMO detection. Jacob Goldberger and Amir Leshem, IEEE Journal of Selected Topics in Signal Processing, vol. 5, pp. 1486-1496, 2011.
• MIMO detection for high-order QAM based on a Gaussian tree approximation. Jacob Goldberger and Amir Leshem. IEEE Trans. Information Theory , vol. 57, pp. 4973-82, 2011.
• Pseudo prior belief propagation for densely connected discrete graphs. Jacob Goldberger and Amir Leshem. IEEE Information Theory Workshop (ITW), 2010.
• A Gaussian tree approximation for integer least-squares. Jacob Goldberger and Amir Leshem. Neural Information Processing Systems (NIPS), 2009.
• Serial schedules for belief-propagation: analysis of convergence time. Jacob Goldberger and Haggai Kfir, IEEE Trans. on Information Theory, pp. 1316-19, 2008.
• Efficient serial message-passing schedules for LDPC decoding. Eran Sharon, Simon Litsyn and Jacob Goldberger, IEEE Trans. on Information Theory, pp. 4076-91, 2007.
• Solving Sudoku using combined message passing algorithms. Jacob Goldberger,Technical Reort TR-BIU-ENG-2007-05-03, Engineering School, Bar-Ilan Univ., 2007.
• Sequentially finding the N-best list in hidden Markov models. Dennis Nilsson and Jacob Goldberger, International Conference on Artificial Intelligence (IJCAI), 2001.

language processing

• The power of summary-source alignments, Ori Ernst, Ori Shapira, Aviv Slobodkin, Sharon Adar, Mohit Bansal, Jacob Goldberger, Ran Levy, and Ido Dagan, Findings of  ACL, 2024.
• Peek across: Improving multi-document modeling via cross-document question-answering, Avi Caciularu, Matthew Peters, Jacob Goldberger, Ido Dagan and Arman Cohan, Annual Meeting of the Association for Computational Linguistics (ACL), 2023.
• Conformal nucleus sampling,  Shauli Ravfogel, Yoav Goldberg and Jacob Goldberger, Findings of ACL, 2023.
• Long context question answering via supervised contrastive learning, Avi Caciularu, Ido Dagan, Jacob Goldberger and Arman Cohan, The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2022.
• A proposition-level clustering approach for multi-document summarization, Ori Ernst, Avi Caciularu, Ori Shapira, Ramakanth Pasunuru, Mohit Bansal, Jacob Goldberger and Ido Dagan, The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) , 2022.
• Summary-source proposition-level alignment: task, datasets and supervised baseline, Ori Ernst, Ori Shapira, Ramakanth Pasunuru, Michael Lepioshkin, Jacob Goldberger, Mohit Bansal and Ido Dagan, Conference on Computational Natural Language Learning (CoNLL), 2021. Best paper runner up.
• Denoising word embeddings by averaging in a shared space,  Avi Caciularu,  Ido Dagan and Jacob Goldberger, *SEM: Joint Conference on Lexical and Computational Semantics, 2021.
• A locally linear procedure for word translation. Soham Dan, Hagai Taitelbaum and Jacob Goldberger, The International Conference on Computational Linguistics (COLING), 2020.
• Unsupervised distillation of syntactic information from contextualized word representations. Shauli Ravfogel, Yanai Elazar, Jacob Goldberger and Yoav Goldberg, Black-boxNLP EMNLP Workshop, 2020.
• Multilingual word translation using auxiliary languages. Hagai Taitelbaum, Gal Chechik and Jacob Goldberger, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019.
• A multi-pairwise extension of procrustes analysis for multilingual word translation. Hagai Taitelbaum, Gal Chechik and Jacob Goldberger, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019.
• Aligning vector-spaces with noisy supervised lexicon. Noa Yehezkel Lubin, Jacob Goldberger and Yoav Goldberg, North American Chapter of the Association for Computational Linguistics (NAACL), 2019.
• Self-normalization properties of language modeling. Jacob Goldberger and Oren Melamud, The International Conference on Computational Linguistics (COLING), 2018.
• A simple language model based on PMI matrix approximations. Oren Melamud, Ido Dagan and Jacob Goldberger, Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2017
• Information-theory interpretation of the skip-gram negative-sampling objective function. Oren Melamud and Jacob Goldberger, Annual Meeting of the Association for Computational Linguistics (ACL), 2017.
• Context2vec: learning generic context embedding with bidirectional LSTM. Oren Melamud, Jacob Goldberger and Ido Dagan, Conference on Computational Natural Language Learning (CoNLL) , 2016.
• Efficient global learning of entailment graphs. Jonathan Berant, Noga Alon, Ido Dagan and Jacob Goldberger, Computational Linguistics, vol. 41:2,pp. 221–263. 2015.
• Learning to exploit structured resources for lexical inference. Vered Shwartz, Omer Levy, Ido Dagan and Jacob Goldberger, Conference on Computational Natural Language Learning (CoNLL) , 2015.
• Modeling word meaning in context with substitute vectors. Oren Melamud, Ido Dagan and Jacob Goldberger, North American Chapter of the ACL (NAACL) , 2015.
• Probabilistic modeling of joint-context in distributional similarity. Oren Melamud, Ido Dagan, Jacob Goldberger, Idan Szpektor and Deniz Yuret, Conference on Computational Natural Language Learning (CoNLL) , 2014. Best paper runner up.
• Focused entailment graphs for open IE propositions. Omer Levy, Ido Dagan, Jacob Goldberger, Conference on Computational Natural Language Learning (CoNLL) , 2014.
• A two level model for context sensitive inference rules. Oren Melamud, Jonathan Berant, Ido Dagan, Jacob Goldberger and Idan Szpektor, Annual Meeting of the Association for Computational Linguistics (ACL), 2013. Best paper runner up.
• Using lexical expansion to learn inference rules from sparse data. Oren Melamud, Ido Dagan, Jacob Goldberger and Idan Szpektor, Annual Meeting of the Association for Computational Linguistics (ACL), short paper, 2013.
• Efficient tree-based approximation for entailment graph learning. Jonathan Berant, Ido Dagan, Meni Adler and Jacob Goldberger. Annual Meeting of the Association for Computational Linguistics (ACL), 2012.
• A Probabilistic Lexical Model for Ranking Textual Inferences. Eyal Shnarch, Ido Dagan, Jacob Goldberger *SEM (Joint Conference on Lexical and Computational Semantics), 2012.
• Learning entailment relations by global graph structure optimization. Jonathan Berant, Ido Dagan and Jacob Goldberger. Computational Linguistics, vol. 38:1, pp. 1-39, 2012.
• Towards a probabilistic model for lexical entailment. Eyal Shnarch, Jacob Goldberger and Ido Dagan. TextInfer-Workshop on Textual Entailment, 2011.
• Global learning of typed entailment rules. Jonathan Berant, Ido Dagan and Jacob Goldberger. Annual Meeting of the Association for Computational Linguistics (ACL), 2011. Received the best student long paper award. Received the best student long paper award.
• A probabilistic modeling framework for lexical entailment. Eyal Shnarch, Jacob Goldberger and Ido Dagan, Annual Meeting of the Association for Computational Linguistics (ACL), 2011.
• Global learning of focused entailment graphs. Jonathan Berant, Ido Dagan and Jacob Goldberger. Annual Meeting of the Association for Computational Linguistics (ACL), 2010.
• Contextual preferences. Idan Szpektor, Ido Dagan, Roy Bar-Haim and Jacob Goldberger. Annual Meeting of the Association for Computational Linguistics (ACL), 2008.

speech processing

• Domain adaptation using suitable pseudo labels for speech enhancement and dereverberation, Lior Frenkel, Shlomi Chazan and Jacob Goldberger, IEEE Transactions on Audio, Speech and Language Processing,  vol.  36. pp.  1226-1236, 2024.
• Domain adaptation for speech enhancement in a large domain gap, Lior Frenkel, Jacob Goldberger and  Shlomo Chazan,  Interspeech, 2023.
• Speech enhancement with mixture of deep experts with clean clustering pre-training, Shlomo E. Chazan, Jacob Goldberger and Sharon Gannot, IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2021.
• Dynamically localizing multiple speakers based on the time-frequency domain, Hodaya Hammer, Shlomo E. Chazan, Jacob Goldberger and Sharon Gannot, EURASIP Journal on Audio, Speech, and Music Processing, 2021.
• A composite DNN architecture for speech enhancement. Yochai Yemini, Shlomo E. Chazan, Jacob Goldberger and Sharon Gannot, IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2020.
• Multi-microphone speaker separation based on deep DOA estimation. Shlomo E. Chazan, Hodaya Hammer, Gershon Hazan, Jacob Goldberger and Sharon Gannot, The European Signal Processing Conference (EUSIPCO), 2019.
• Formant estimation and tracking: a deep learning approach. Yehoshua Dissen, Jacob Goldberger and Joseph Keshet, The Journal of the Acoustical Society of America, vol. 145(2), pp. 642-653, 2019.
• Attention-based neural network for joint diarization and speaker extraction Shlomo E. Chazan, Sharon Gannot and Jacob Goldberger, Int. Workshop on Acoustic Signal Enhancement (IWAENC), 2018.
• Adding new classes without access to the original training data with applications to language identification. Hagai Taitelbaum, Ehud Ben-Reuven and Jacob Goldberger, INTERSPEECH, 2018.
• LCMV beamformer with DNN-based multichannel concurrent speakers detector. Shlomo E. Chazan, Jacob Goldberger and Sharon Gannot, European Signal Processing Conference (EUSIPCO), 2018.
• Speech dereverberation using fully convolutional networks. Ori Ernst, Shlomo E. Chazan, Sharon Gannot and Jacob Goldberger, European Signal Processing Conference (EUSIPCO), 2018.
• Training strategies for deep latent models and applications to speech presence probability estimation. Shlomo E. Chazan, Sharon Gannot and Jacob Goldberger, Int. Conference on Latent Variable Analysis and Signal Separation (LVA/ICA), 2018.
• DNN-based concurrent speaker detector and its application to speaker extraction with LCMV beamforming. Shlomo E. Chazan, Jacob Goldberger and Sharon Gannot, IEEE Int. Conference on Acoustic, Speech and Signal Processing (ICASSP), 2018
• Deep recurrent mixture of experts for speech enhancement. Shlomo E. Chazan, Jacob Goldberger and Sharon Gannot, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) , 2017.
• Successive relative transfer function identification using single microphone speech enhancement. Dany Cherkassky, Shlomo E. Chazan, Jacob Goldberger and Sharon Gannot, The European Signal Processing Conference (EUSIPCO) , 2017.
• Speaker extraction using LCMV beamformer with DNN-based SPP and RTF identification scheme. Ariel Malek, Shlomi Chazan, Ilan Malka, Vladimir Tourbabin, Jacob Goldberger, Eli Tzirkel-Hancock and Sharon Gannot, The European Signal Processing Conference (EUSIPCO) , 2017.
• A semisupervised approach for language identification based on ladder networks. Ehud Ben-Reuven and Jacob Goldberger, Odyssey Speaker and Language Recognition Workshop, 2016.
• Intra-cluster training strategy for deep learning with applications to language identification. Alan Joseph Bekker, Irit Opher, Itsik Lapidot and Jacob Goldberger, IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2016.
• A pre-training approach for deep neural network with application to speech enhancement. Shlomo E. Chazan, Sharon Gannot and Jacob Goldberger, Int. Workshop on Acoustic Signal Enhancement (IWAENC), 2016. Best student paper award
• A hybrid approach for speech enhancement using MoG model and neural network phoneme classifier. Shlomo E. Chazan, Jacob Goldberger and Sharon Gannot, IEEE Trans. on Audio, Speech and Language Processing, vol. 24(12), pp. 2516-2530, 2016.
• Automatic acoustic detection of the red palm weevil. Joel Pinhas, Victoria Soroker, Amots Hetzroni, Amos Mizrach, Mina Teicher and Jacob Goldberger, Computers and Electronics in Agriculture, vol. 63, pages 131-139, 2008.
• A distance measure between GMMs based on the unscented transform and its application to speaker recognition. Jacob Goldberger and Hagai Aronowitz, Eurospeech, 2005.
• Segmental modeling using a continuous mixture of non-parametric models. Jacob Goldberger, David Burshtein and Horacio Franco, IEEE Trans. Speech Audio Proc, vol. 7, pp. 262-271, 1999.
• Scaled random trajectory segmental models. Jacob Goldberger and David Burshtein, Computer Speech and Language 12, pp. 51-73, 1998.
• Scaled random trajectory segmental models. Jacob Goldberger and David Burshtein, IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 1998.
• Segmental modelling using a continuous mixture of non-parametric models. Jacob Goldberger, David Burshtein and Horacio Franco, EuroSpeech, 1997.

information theory in machine learning

• Information-bottleneck based on the Jensen-Shannon divergence with applications to pairwise clustering. Jacob Goldberger and Yaniv Opochinsky, IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2019.
• Information-theory interpretation of the skip-gram negative-sampling objective function. Oren Melamud and Jacob Goldberger, Annual Meeting of the Association for Computational Linguistics (ACL), 2017.
• Pairwise clustering based on the mutual-information criterion. Amir Alush, Avishay Friedman and Jacob Goldberger, Neurocomputing vol. 182, pp. 284-293, 2016.
• Unsupervised Feature Selection based on Non-Parametric Mutual Information. Lev Faivishevsky and Jacob Goldberger, IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2012.
• Dimensionality reduction based on non-parametric mutual information. Lev Faivishevsky and Jacob Goldberger, Neurocomputing, vol. 80, pp. 31-37, 2012.
• Mutual information based dimensionality reduction with application to non-linear regression. Lev Faivishevsky and Jacob Goldberger, IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2010.
• A nonparametric information theoretic clustering algorithm.  Lev Faivishevsky and Jacob Goldberger, International Conference Machine Learning (ICML), 2010.
• ICA based on a smooth estimation of the differential entropy. Lev Faivishevsky and Jacob Goldberger, Neural Information Processing Systems (NIPS), 2008.
• An information theoretic framework for unsupervised image clustering. Jacob Goldberger, Shiri Gordon and Hayit Greenspan, IEEE Trans. on Image Processing, vol. 15, pp. 449-458, 2006.
• Applying the information bottleneck principle to unsupervised clustering of discrete and continuous image representations. Shiri Gordon, Jacob Goldberger and Hayit Greenspan, International Conference on Computer Vision (ICCV), 2003.

wireless communication

• MIMO detection based on averaging Gaussian projections. Jacob Goldberger, IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2014.
• Improved MIMO detection based on successive tree approximations. Jacob Goldberger, IEEE Int. Symposium on Information Theory (ISIT), 2013. C code
• Iterative tomographic solution of integer least squares problems with applications to MIMO detection. Jacob Goldberger and Amir Leshem, IEEE Journal of Selected Topics in Signal Processing, vol. 5, pp. 1486-1496, 2011.
• MIMO detection for high-order QAM based on a Gaussian tree approximation. Jacob Goldberger and Amir Leshem. IEEE Trans. Information Theory , vol. 57, pp. 4973-82, 2011.
• Pseudo prior belief propagation for densely connected discrete graphs. Jacob Goldberger and Amir Leshem. IEEE Information Theory Workshop (ITW), 2010.
• A Gaussian tree approximation for integer least-squares. Jacob Goldberger and Amir Leshem. Neural Information Processing Systems (NIPS), 2009.
• MIMO decoding based on stochastic reconstruction from multiple projections. Amir Leshem and Jacob Goldberger. IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2009.

error correcting codes

• perm2vec: Attentive graph permutation selection for decoding of error correction codes Avi Caciularu, Nir Raviv, Tomer Raviv, Jacob Goldberger and Yair Be'ery, IEEE J. Selected Areas Communication, vol. 39(1), pp. 79-88, 2021.
• Serial schedules for belief-propagation: analysis of convergence time. Jacob Goldberger and Haggai Kfir, IEEE Trans. on Information Theory, pp. 1316-19, 2008.
• Efficient serial message-passing schedules for LDPC decoding. Eran Sharon, Simon Litsyn and Jacob Goldberger, IEEE Trans. on Information Theory, pp. 4076-91, 2007.
• Convergence analysis of message-passing schedules for LDPC decoding. Eran Sharon, Simon Litsyn and Jacob Goldberger. The 4th International Symposium on Turbo Codes, 2006
• An efficient message-passing schedule for LDPC decoding. Eran Sharon, Simon Litsyn and Jacob Goldberger. Proc. Electrical and Electronic Engineers in Israel, 2004.

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