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Personalized Federated Learning using Hypernetworks


Mar 08, 2021
Aviv Shamsian, Aviv Navon, Ethan Fetaya, Gal Chechik


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GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning


Feb 15, 2021
Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, Ethan Fetaya


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Position-Agnostic Multi-Microphone Speech Dereverberation


Oct 22, 2020
Yochai Yemini, Ethan Fetaya, Haggai Maron, Sharon Gannot


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On Size Generalization in Graph Neural Networks


Oct 17, 2020
Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik, Haggai Maron


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Learning the Pareto Front with Hypernetworks


Oct 08, 2020
Aviv Navon, Aviv Shamsian, Gal Chechik, Ethan Fetaya


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Auxiliary Learning by Implicit Differentiation


Jun 22, 2020
Aviv Navon, Idan Achituve, Haggai Maron, Gal Chechik, Ethan Fetaya


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Restoration of Fragmentary Babylonian Texts Using Recurrent Neural Networks


Mar 04, 2020
Ethan Fetaya, Yonatan Lifshitz, Elad Aaron, Shai Gordin


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On Learning Sets of Symmetric Elements


Feb 20, 2020
Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya


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Conditional Generative Models are not Robust


Jun 04, 2019
Ethan Fetaya, Jörn-Henrik Jacobsen, Richard Zemel


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Evaluating and Calibrating Uncertainty Prediction in Regression Tasks


May 30, 2019
Dan Levi, Liran Gispan, Niv Giladi, Ethan Fetaya


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On the Universality of Invariant Networks


Jan 27, 2019
Haggai Maron, Ethan Fetaya, Nimrod Segol, Yaron Lipman


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Neural Guided Constraint Logic Programming for Program Synthesis


Oct 26, 2018
Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Matthew Might, Raquel Urtasun, Richard Zemel

* To appear in NIPS 2018 

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Incremental Few-Shot Learning with Attention Attractor Networks


Oct 16, 2018
Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel


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Reviving and Improving Recurrent Back-Propagation


Aug 13, 2018
Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard Zemel


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Neural Relational Inference for Interacting Systems


Jun 06, 2018
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel

* ICML (2018). Code available under https://github.com/ethanfetaya/NRI 

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Inference in Probabilistic Graphical Models by Graph Neural Networks


May 25, 2018
KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard Zemel, Xaq Pitkow


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Learning Discrete Weights Using the Local Reparameterization Trick


Feb 02, 2018
Oran Shayer, Dan Levi, Ethan Fetaya

* ICLR 2018 

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Human Pose Estimation using Deep Consensus Voting


Mar 27, 2016
Ita Lifshitz, Ethan Fetaya, Shimon Ullman


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Unsupervised Ensemble Learning with Dependent Classifiers


Feb 23, 2016
Ariel Jaffe, Ethan Fetaya, Boaz Nadler, Tingting Jiang, Yuval Kluger


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Learning Local Invariant Mahalanobis Distances


Feb 04, 2015
Ethan Fetaya, Shimon Ullman


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Graph Approximation and Clustering on a Budget


Jun 10, 2014
Ethan Fetaya, Ohad Shamir, Shimon Ullman


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