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:
- 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.
I'm a member of the Bar-Ilan Data Science Institute.
My Erdös Number is 2.