| Comparative analysis of approximate blocking techniques for entity resolution G Papadakis, J Svirsky, A Gal, T Palpanas Proceedings of the VLDB Endowment 9 (9), 684-695, 2016 | 193 | 2016 |
| Differentiable unsupervised feature selection based on a gated laplacian O Lindenbaum, U Shaham, E Peterfreund, J Svirsky, N Casey, Y Kluger Advances in neural information processing systems 34, 1530-1542, 2021 | 35 | 2021 |
| Deep unsupervised feature selection by discarding nuisance and correlated features U Shaham, O Lindenbaum, J Svirsky, Y Kluger Neural Networks 152, 34-43, 2022 | 22 | 2022 |
| Let the data choose its features: Differentiable unsupervised feature selection O Lindenbaum, U Shaham, J Svirsky, E Peterfreund, Y Kluger arXiv preprint arXiv:2007.04728, 2020 | 6 | 2020 |
| Interpretable deep clustering J Svirsky, O Lindenbaum Proceedings of the 41st International Conference on Machine Learning (ICML), 2023 | 5 | 2023 |
| Deep ordinal regression using optimal transport loss and unimodal output probabilities U Shaham, I Zaidman, J Svirsky arXiv preprint arXiv:2011.07607, 2020 | 5 | 2020 |
| Sg-vad: Stochastic gates based speech activity detection J Svirsky, O Lindenbaum ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 2 | 2023 |
| Discovery of Single Independent Latent Variable U Shaham, J Svirsky, O Katz, R Talmon Advances in Neural Information Processing Systems 36, 2022 | 2 | 2022 |
| Sparse Binarization for Fast Keyword Spotting J Svirsky, U Shaham, O Lindenbaum Proc. Interspeech 2024, 2024 | | 2024 |
| Self Supervised Correlation-based Permutations for Multi-View Clustering R Eisenberg, J Svirsky, O Lindenbaum arXiv preprint arXiv:2402.16383, 2024 | | 2024 |
| Interpretable Deep Clustering for Tabular Data J Svirsky, O Lindenbaum Forty-first International Conference on Machine Learning, 0 | | |
Ofir LindenbaumAssistant Professor at Bar Ilan UniversityVerified email at biu.ac.il
