Jacob Goldberger
יעקב גולדברגר
Faculty of Engineering
Bar Ilan University, Israel
jacob.goldberger at biu.ac.il
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.
I'm actively looking for strong and motivated PhD and Msc students to join my lab. Check out my recent publications to get an idea of the topics i'm most interested in. Information for prospective students
My Google Scholar page, DBLP page and CV
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.