statistical machine learning and deep learning algorithms
My research deals with developing and analyzing efficient and effective machine learning and deep learning algorithms and applying these algorithms to a large variety of applications such as computer vision, signal processing, medical image processing, speech processing, and natural language processing.
Recent Deep Learning Publications:
- 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.
- 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.
I'm a member of the Bar-Ilan Data Science Institute.
My Erdös Number is 2.