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ORCIDEthan Fetaya
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- affiliation: Bar-Ilan University, Istrael
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Journal Articles
- 2024
[j4]Eyal Betzalel
, Coby Penso, Ethan Fetaya:
Evaluation Metrics for Generative Models: An Empirical Study. Mach. Learn. Knowl. Extr. 6(3): 1531-1544 (2024)- 2023
- [j3]Idan Achituve, Wenbo Wang, Ethan Fetaya, Amir Leshem:
Communication Efficient Distributed Learning Over Wireless Channels. IEEE Signal Process. Lett. 30: 1402-1406 (2023) - 2022
- [j2]Dan Levi, Liran Gispan, Niv Giladi, Ethan Fetaya:
Evaluating and Calibrating Uncertainty Prediction in Regression Tasks. Sensors 22(15): 5540 (2022) - 2020
- [j1]Ethan Fetaya, Yonatan Lifshitz, Elad Aaron, Shai Gordin:
Restoration of fragmentary Babylonian texts using recurrent neural networks. Proc. Natl. Acad. Sci. USA 117(37): 22743-22751 (2020)
Conference and Workshop Papers
- 2024
- [c38]Yochai Yemini, Aviv Shamsian, Lior Bracha, Sharon Gannot, Ethan Fetaya:
LipVoicer: Generating Speech from Silent Videos Guided by Lip Reading. ICLR 2024 - [c37]Idan Achituve, Idit Diamant, Arnon Netzer, Gal Chechik, Ethan Fetaya:
Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning. ICML 2024 - [c36]Aviv Navon, Aviv Shamsian, Ethan Fetaya, Gal Chechik, Nadav Dym, Haggai Maron:
Equivariant Deep Weight Space Alignment. ICML 2024 - [c35]Aviv Shamsian, Aviv Navon, David W. Zhang, Yan Zhang, Ethan Fetaya, Gal Chechik, Haggai Maron:
Improved Generalization of Weight Space Networks via Augmentations. ICML 2024 - [c34]Boris Rubenchik, Elior Hadad, Eli Tzirkel, Ethan Fetaya, Sharon Gannot:
Low-Latency Single-Microphone Speaker Separation with Temporal Convolutional Networks Using Speaker Representations. IWAENC 2024: 155-159 - 2023
- [c33]Hila Levi, Guy Heller, Dan Levi, Ethan Fetaya:
Object-Centric Open-Vocabulary Image Retrieval with Aggregated Features. BMVC 2023: 608 - [c32]Lior Bracha, Eitan Shaar, Aviv Shamsian, Ethan Fetaya, Gal Chechik:
DisCLIP: Open-Vocabulary Referring Expression Generation. BMVC 2023: 670-673 - [c31]Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, Haggai Maron:
Equivariant Architectures for Learning in Deep Weight Spaces. ICML 2023: 25790-25816 - [c30]Aviv Shamsian, Aviv Navon, Neta Glazer, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya:
Auxiliary Learning as an Asymmetric Bargaining Game. ICML 2023: 30689-30705 - [c29]Guy Heller, Ethan Fetaya:
Can Stochastic Gradient Langevin Dynamics Provide Differential Privacy for Deep Learning? SaTML 2023: 68-106 - [c28]Idan Achituve, Gal Chechik, Ethan Fetaya:
Guided Deep Kernel Learning. UAI 2023: 11-21 - 2022
- [c27]Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya:
Multi-Task Learning as a Bargaining Game. ICML 2022: 16428-16446 - [c26]Coby Penso, Idan Achituve, Ethan Fetaya:
Functional Ensemble Distillation. NeurIPS 2022 - 2021
- [c25]Aviv Navon, Idan Achituve, Haggai Maron, Gal Chechik, Ethan Fetaya:
Auxiliary Learning by Implicit Differentiation. ICLR 2021 - [c24]Aviv Navon, Aviv Shamsian, Ethan Fetaya, Gal Chechik:
Learning the Pareto Front with Hypernetworks. ICLR 2021 - [c23]Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, Ethan Fetaya:
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning. ICML 2021: 54-65 - [c22]Aviv Shamsian, Aviv Navon, Ethan Fetaya, Gal Chechik:
Personalized Federated Learning using Hypernetworks. ICML 2021: 9489-9502 - [c21]Gilad Yehudai, Ethan Fetaya, Eli A. Meirom, Gal Chechik, Haggai Maron:
From Local Structures to Size Generalization in Graph Neural Networks. ICML 2021: 11975-11986 - [c20]Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya:
On Learning Sets of Symmetric Elements (Extended Abstract). IJCAI 2021: 4794-4798 - [c19]Yochai Yemini, Ethan Fetaya, Haggai Maron, Sharon Gannot:
Scene-Agnostic Multi-Microphone Speech Dereverberation. Interspeech 2021: 1129-1133 - [c18]Idan Achituve, Aviv Shamsian, Aviv Navon, Gal Chechik, Ethan Fetaya:
Personalized Federated Learning With Gaussian Processes. NeurIPS 2021: 8392-8406 - 2020
- [c17]Ethan Fetaya, Jörn-Henrik Jacobsen, Will Grathwohl, Richard S. Zemel:
Understanding the Limitations of Conditional Generative Models. ICLR 2020 - [c16]Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya:
On Learning Sets of Symmetric Elements. ICML 2020: 6734-6744 - 2019
- [c15]KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard S. Zemel, Xaq Pitkow:
Inference in Probabilistic Graphical Models by Graph Neural Networks. ACSSC 2019: 868-875 - [c14]Haggai Maron, Ethan Fetaya, Nimrod Segol, Yaron Lipman:
On the Universality of Invariant Networks. ICML 2019: 4363-4371 - [c13]Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel:
Incremental Few-Shot Learning with Attention Attractor Networks. NeurIPS 2019: 5276-5286 - 2018
- [c12]Oran Shayer, Dan Levi, Ethan Fetaya:
Learning Discrete Weights Using the Local Reparameterization Trick. ICLR (Poster) 2018 - [c11]KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard S. Zemel, Xaq Pitkow:
Inference in probabilistic graphical models by Graph Neural Networks. ICLR (Workshop) 2018 - [c10]Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Raquel Urtasun, Richard S. Zemel:
Leveraging Constraint Logic Programming for Neural Guided Program Synthesis. ICLR (Workshop) 2018 - [c9]Thomas N. Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard S. Zemel:
Neural Relational Inference for Interacting Systems. ICML 2018: 2693-2702 - [c8]Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard S. Zemel:
Reviving and Improving Recurrent Back-Propagation. ICML 2018: 3088-3097 - [c7]Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Matthew Might, Raquel Urtasun, Richard S. Zemel:
Neural Guided Constraint Logic Programming for Program Synthesis. NeurIPS 2018: 1744-1753 - 2017
- [c6]Noa Garnett, Shai Silberstein, Shaul Oron, Ethan Fetaya, Uri Verner, Ariel Ayash, Vlad Goldner, Rafi Cohen, Kobi Horn, Dan Levi:
Real-Time Category-Based and General Obstacle Detection for Autonomous Driving. ICCV Workshops 2017: 198-205 - 2016
- [c5]Ariel Jaffe, Ethan Fetaya, Boaz Nadler, Tingting Jiang, Yuval Kluger:
Unsupervised Ensemble Learning with Dependent Classifiers. AISTATS 2016: 351-360 - [c4]Ita Lifshitz, Ethan Fetaya, Shimon Ullman:
Human Pose Estimation Using Deep Consensus Voting. ECCV (2) 2016: 246-260 - 2015
- [c3]Ethan Fetaya, Ohad Shamir, Shimon Ullman:
Graph Approximation and Clustering on a Budget. AISTATS 2015 - [c2]Dan Levi, Noa Garnett, Ethan Fetaya:
StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation. BMVC 2015: 109.1-109.12 - [c1]Ethan Fetaya, Shimon Ullman:
Learning Local Invariant Mahalanobis Distances. ICML 2015: 162-168
Informal and Other Publications
- 2024
- [i41]Idan Achituve, Idit Diamant, Arnon Netzer, Gal Chechik, Ethan Fetaya:
Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning. CoRR abs/2402.04005 (2024) - [i40]Aviv Shamsian, Aviv Navon, David W. Zhang, Yan Zhang, Ethan Fetaya, Gal Chechik, Haggai Maron:
Improved Generalization of Weight Space Networks via Augmentations. CoRR abs/2402.04081 (2024) - [i39]Neta Glazer, Aviv Navon, Aviv Shamsian, Ethan Fetaya:
Multi Task Inverse Reinforcement Learning for Common Sense Reward. CoRR abs/2402.11367 (2024) - 2023
- [i38]Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, Haggai Maron:
Equivariant Architectures for Learning in Deep Weight Spaces. CoRR abs/2301.12780 (2023) - [i37]Aviv Shamsian, Aviv Navon, Neta Glazer, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya:
Auxiliary Learning as an Asymmetric Bargaining Game. CoRR abs/2301.13501 (2023) - [i36]Idan Achituve, Gal Chechik, Ethan Fetaya:
Guided Deep Kernel Learning. CoRR abs/2302.09574 (2023) - [i35]Lior Bracha, Eitan Shaar, Aviv Shamsian, Ethan Fetaya, Gal Chechik:
DisCLIP: Open-Vocabulary Referring Expression Generation. CoRR abs/2305.19108 (2023) - [i34]Yochai Yemini, Aviv Shamsian, Lior Bracha, Sharon Gannot, Ethan Fetaya:
LipVoicer: Generating Speech from Silent Videos Guided by Lip Reading. CoRR abs/2306.03258 (2023) - [i33]Ariel Lapid, Idan Achituve, Lior Bracha, Ethan Fetaya:
GD-VDM: Generated Depth for better Diffusion-based Video Generation. CoRR abs/2306.11173 (2023) - [i32]Guy Berger, Aviv Navon, Ethan Fetaya:
Learning Discrete Weights and Activations Using the Local Reparameterization Trick. CoRR abs/2307.01683 (2023) - [i31]Hila Levi, Guy Heller, Dan Levi, Ethan Fetaya:
Object-Centric Open-Vocabulary Image-Retrieval with Aggregated Features. CoRR abs/2309.14999 (2023) - [i30]Aviv Navon, Aviv Shamsian, Ethan Fetaya, Gal Chechik, Nadav Dym, Haggai Maron:
Equivariant Deep Weight Space Alignment. CoRR abs/2310.13397 (2023) - [i29]Aviv Shamsian, David W. Zhang, Aviv Navon, Yan Zhang, Miltiadis Kofinas, Idan Achituve, Riccardo Valperga, Gertjan J. Burghouts, Efstratios Gavves, Cees G. M. Snoek, Ethan Fetaya, Gal Chechik, Haggai Maron:
Data Augmentations in Deep Weight Spaces. CoRR abs/2311.08851 (2023) - 2022
- [i28]Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya:
Multi-Task Learning as a Bargaining Game. CoRR abs/2202.01017 (2022) - [i27]Coby Penso, Idan Achituve, Ethan Fetaya:
Functional Ensemble Distillation. CoRR abs/2206.02183 (2022) - [i26]Eyal Betzalel, Coby Penso, Aviv Navon, Ethan Fetaya:
A Study on the Evaluation of Generative Models. CoRR abs/2206.10935 (2022) - [i25]Idan Achituve, Wenbo Wang, Ethan Fetaya, Amir Leshem:
Communication Efficient Distributed Learning over Wireless Channels. CoRR abs/2209.01682 (2022) - 2021
- [i24]Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, Ethan Fetaya:
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning. CoRR abs/2102.07868 (2021) - [i23]Aviv Shamsian, Aviv Navon, Ethan Fetaya, Gal Chechik:
Personalized Federated Learning using Hypernetworks. CoRR abs/2103.04628 (2021) - [i22]Idan Achituve, Aviv Shamsian, Aviv Navon, Gal Chechik, Ethan Fetaya:
Personalized Federated Learning with Gaussian Processes. CoRR abs/2106.15482 (2021) - [i21]Guy Heller, Ethan Fetaya:
Can Stochastic Gradient Langevin Dynamics Provide Differential Privacy for Deep Learning? CoRR abs/2110.05057 (2021) - 2020
- [i20]Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya:
On Learning Sets of Symmetric Elements. CoRR abs/2002.08599 (2020) - [i19]Ethan Fetaya, Yonatan Lifshitz, Elad Aaron, Shai Gordin:
Restoration of Fragmentary Babylonian Texts Using Recurrent Neural Networks. CoRR abs/2003.01912 (2020) - [i18]Aviv Navon, Idan Achituve, Haggai Maron, Gal Chechik, Ethan Fetaya:
Auxiliary Learning by Implicit Differentiation. CoRR abs/2007.02693 (2020) - [i17]Aviv Navon, Aviv Shamsian, Gal Chechik, Ethan Fetaya:
Learning the Pareto Front with Hypernetworks. CoRR abs/2010.04104 (2020) - [i16]Gilad Yehudai, Ethan Fetaya, Eli A. Meirom, Gal Chechik, Haggai Maron:
On Size Generalization in Graph Neural Networks. CoRR abs/2010.08853 (2020) - [i15]Yochai Yemini, Ethan Fetaya, Haggai Maron, Sharon Gannot:
Position-Agnostic Multi-Microphone Speech Dereverberation. CoRR abs/2010.11875 (2020) - 2019
- [i14]Haggai Maron, Ethan Fetaya, Nimrod Segol, Yaron Lipman:
On the Universality of Invariant Networks. CoRR abs/1901.09342 (2019) - [i13]Dan Levi, Liran Gispan, Niv Giladi, Ethan Fetaya:
Evaluating and Calibrating Uncertainty Prediction in Regression Tasks. CoRR abs/1905.11659 (2019) - [i12]Ethan Fetaya, Jörn-Henrik Jacobsen, Richard S. Zemel:
Conditional Generative Models are not Robust. CoRR abs/1906.01171 (2019) - 2018
- [i11]Thomas N. Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard S. Zemel:
Neural Relational Inference for Interacting Systems. CoRR abs/1802.04687 (2018) - [i10]Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard S. Zemel:
Reviving and Improving Recurrent Back-Propagation. CoRR abs/1803.06396 (2018) - [i9]KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard S. Zemel, Xaq Pitkow:
Inference in Probabilistic Graphical Models by Graph Neural Networks. CoRR abs/1803.07710 (2018) - [i8]Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Matthew Might, Raquel Urtasun, Richard S. Zemel:
Neural Guided Constraint Logic Programming for Program Synthesis. CoRR abs/1809.02840 (2018) - [i7]Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel:
Incremental Few-Shot Learning with Attention Attractor Networks. CoRR abs/1810.07218 (2018) - 2017
- [i6]Oran Shayer, Dan Levi, Ethan Fetaya:
Learning Discrete Weights Using the Local Reparameterization Trick. CoRR abs/1710.07739 (2017) - 2016
- [i5]Ita Lifshitz, Ethan Fetaya, Shimon Ullman:
Human Pose Estimation using Deep Consensus Voting. CoRR abs/1603.08212 (2016) - 2015
- [i4]Ethan Fetaya, Shimon Ullman:
Learning Local Invariant Mahalanobis Distances. CoRR abs/1502.01176 (2015) - [i3]Ariel Jaffe, Ethan Fetaya, Boaz Nadler, Tingting Jiang, Yuval Kluger:
Unsupervised Ensemble Learning with Dependent Classifiers. CoRR abs/1510.05830 (2015) - 2014
- [i2]Ethan Fetaya, Ohad Shamir, Shimon Ullman:
Graph Approximation and Clustering on a Budget. CoRR abs/1406.2602 (2014) - 2011
- [i1]Ethan Fetaya:
Homological Error Correcting Codes and Systolic Geometry. CoRR abs/1108.2886 (2011)
Coauthor Index
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