Jonathan Svirsky

According to our database, Jonathan Svirsky authored at least 12 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Sparse Binarization for Fast Keyword Spotting.

Jonathan Svirsky, Uri Shaham, Ofir Lindenbaum

CoRR, 2024

Self Supervised Correlation-based Permutations for Multi-View Clustering.

Ran Eisenberg, Jonathan Svirsky, Ofir Lindenbaum

CoRR, 2024

Interpretable Deep Clustering for Tabular Data.

Jonathan Svirsky, Ofir Lindenbaum

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Interpretable Deep Clustering.

Jonathan Svirsky, Ofir Lindenbaum

CoRR, 2023

SG-VAD: Stochastic Gates Based Speech Activity Detection.

Jonathan Svirsky, Ofir Lindenbaum

Proceedings of the IEEE International Conference on Acoustics, 2023

2022

Deep unsupervised feature selection by discarding nuisance and correlated features.

Uri Shaham, Ofir Lindenbaum, Jonathan Svirsky, Yuval Kluger

Neural Networks, 2022

Discovery of Single Independent Latent Variable.

Uri Shaham, Jonathan Svirsky, Ori Katz, Ronen Talmon

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Single Independent Component Recovery and Applications.

Uri Shaham, Jonathan Svirsky, Ori Katz, Ronen Talmon

CoRR, 2021

Differentiable Unsupervised Feature Selection based on a Gated Laplacian.

Ofir Lindenbaum, Uri Shaham, Erez Peterfreund, Jonathan Svirsky, Nicolas Casey, Yuval Kluger

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Deep Ordinal Regression using Optimal Transport Loss and Unimodal Output Probabilities.

Uri Shaham, Jonathan Svirsky

CoRR, 2020

Let the Data Choose its Features: Differentiable Unsupervised Feature Selection.

Ofir Lindenbaum, Uri Shaham, Jonathan Svirsky, Erez Peterfreund, Yuval Kluger

CoRR, 2020

2016

Comparative Analysis of Approximate Blocking Techniques for Entity Resolution.

George Papadakis, Jonathan Svirsky, Avigdor Gal, Themis Palpanas

Proc. VLDB Endow., 2016