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ORCIDJonathan Svirsky
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2020 – today
- 2024
[c4]Jonathan Svirsky, Ofir Lindenbaum:
Interpretable Deep Clustering for Tabular Data. ICML 2024- [i8]Ran Eisenberg, Jonathan Svirsky, Ofir Lindenbaum:
Self Supervised Correlation-based Permutations for Multi-View Clustering. CoRR abs/2402.16383 (2024) - [i7]Jonathan Svirsky, Uri Shaham, Ofir Lindenbaum:
Sparse Binarization for Fast Keyword Spotting. CoRR abs/2406.06634 (2024) - 2023
- [c3]Jonathan Svirsky, Ofir Lindenbaum:
SG-VAD: Stochastic Gates Based Speech Activity Detection. ICASSP 2023: 1-5 - [i6]Jonathan Svirsky, Ofir Lindenbaum:
Interpretable Deep Clustering. CoRR abs/2306.04785 (2023) - 2022
- [j2]Uri Shaham
, Ofir Lindenbaum, Jonathan Svirsky, Yuval Kluger:
Deep unsupervised feature selection by discarding nuisance and correlated features. Neural Networks 152: 34-43 (2022) - [c2]Uri Shaham, Jonathan Svirsky, Ori Katz, Ronen Talmon:
Discovery of Single Independent Latent Variable. NeurIPS 2022 - [i5]Jonathan Svirsky, Ofir Lindenbaum:
SG-VAD: Stochastic Gates Based Speech Activity Detection. CoRR abs/2210.16022 (2022) - 2021
- [c1]Ofir Lindenbaum, Uri Shaham, Erez Peterfreund, Jonathan Svirsky, Nicolas Casey, Yuval Kluger:
Differentiable Unsupervised Feature Selection based on a Gated Laplacian. NeurIPS 2021: 1530-1542 - [i4]Uri Shaham, Ofir Lindenbaum, Jonathan Svirsky, Yuval Kluger:
Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features. CoRR abs/2110.05306 (2021) - [i3]Uri Shaham, Jonathan Svirsky, Ori Katz, Ronen Talmon:
Single Independent Component Recovery and Applications. CoRR abs/2110.05887 (2021) - 2020
- [i2]Ofir Lindenbaum, Uri Shaham, Jonathan Svirsky, Erez Peterfreund, Yuval Kluger:
Let the Data Choose its Features: Differentiable Unsupervised Feature Selection. CoRR abs/2007.04728 (2020) - [i1]Uri Shaham, Jonathan Svirsky:
Deep Ordinal Regression using Optimal Transport Loss and Unimodal Output Probabilities. CoRR abs/2011.07607 (2020)
2010 – 2019
- 2016
- [j1]George Papadakis, Jonathan Svirsky, Avigdor Gal, Themis Palpanas:
Comparative Analysis of Approximate Blocking Techniques for Entity Resolution. Proc. VLDB Endow. 9(9): 684-695 (2016)
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