Ethan Fetaya

According to our database, Ethan Fetaya authored at least 48 papers between 2011 and 2024.

Collaborative distances:

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

2024

Multi Task Inverse Reinforcement Learning for Common Sense Reward.

Neta Glazer, Aviv Navon, Aviv Shamsian, Ethan Fetaya

CoRR, 2024

Improved Generalization of Weight Space Networks via Augmentations.

Aviv Shamsian, Aviv Navon, David W. Zhang, Yan Zhang, Ethan Fetaya, Gal Chechik, Haggai Maron

CoRR, 2024

Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning.

Idan Achituve, Idit Diamant, Arnon Netzer, Gal Chechik, Ethan Fetaya

CoRR, 2024

2023

Communication Efficient Distributed Learning Over Wireless Channels.

Idan Achituve, Wenbo Wang, Ethan Fetaya, Amir Leshem

IEEE Signal Process. Lett., 2023

Data Augmentations in Deep Weight Spaces.

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

CoRR, 2023

Equivariant Deep Weight Space Alignment.

Aviv Navon, Aviv Shamsian, Ethan Fetaya, Gal Chechik, Nadav Dym, Haggai Maron

CoRR, 2023

Learning Discrete Weights and Activations Using the Local Reparameterization Trick.

Guy Berger, Aviv Navon, Ethan Fetaya

CoRR, 2023

GD-VDM: Generated Depth for better Diffusion-based Video Generation.

Ariel Lapid, Idan Achituve, Lior Bracha, Ethan Fetaya

CoRR, 2023

LipVoicer: Generating Speech from Silent Videos Guided by Lip Reading.

Yochai Yemini, Aviv Shamsian, Lior Bracha, Sharon Gannot, Ethan Fetaya

CoRR, 2023

Guided Deep Kernel Learning.

Idan Achituve, Gal Chechik, Ethan Fetaya

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Auxiliary Learning as an Asymmetric Bargaining Game.

Aviv Shamsian, Aviv Navon, Neta Glazer, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya

Proceedings of the International Conference on Machine Learning, 2023

Equivariant Architectures for Learning in Deep Weight Spaces.

Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, Haggai Maron

Proceedings of the International Conference on Machine Learning, 2023

Object-Centric Open-Vocabulary Image Retrieval with Aggregated Features.

Hila Levi, Guy Heller, Dan Levi, Ethan Fetaya

Proceedings of the 34th British Machine Vision Conference 2023, 2023

DisCLIP: Open-Vocabulary Referring Expression Generation.

Lior Bracha, Eitan Shaar, Aviv Shamsian, Ethan Fetaya, Gal Chechik

Proceedings of the 34th British Machine Vision Conference 2023, 2023

2022

Evaluating and Calibrating Uncertainty Prediction in Regression Tasks.

Dan Levi, Liran Gispan, Niv Giladi, Ethan Fetaya

Sensors, 2022

A Study on the Evaluation of Generative Models.

Eyal Betzalel, Coby Penso, Aviv Navon, Ethan Fetaya

CoRR, 2022

Functional Ensemble Distillation.

Coby Penso, Idan Achituve, Ethan Fetaya

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

Multi-Task Learning as a Bargaining Game.

Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya

Proceedings of the International Conference on Machine Learning, 2022

2021

Can Stochastic Gradient Langevin Dynamics Provide Differential Privacy for Deep Learning?

Guy Heller, Ethan Fetaya

CoRR, 2021

Personalized Federated Learning With Gaussian Processes.

Idan Achituve, Aviv Shamsian, Aviv Navon, Gal Chechik, Ethan Fetaya

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

Scene-Agnostic Multi-Microphone Speech Dereverberation.

Yochai Yemini, Ethan Fetaya, Haggai Maron, Sharon Gannot

Proceedings of the Interspeech 2021, 22nd Annual Conference of the International Speech Communication Association, Brno, Czechia, 30 August, 2021

On Learning Sets of Symmetric Elements (Extended Abstract).

Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

From Local Structures to Size Generalization in Graph Neural Networks.

Gilad Yehudai, Ethan Fetaya, Eli A. Meirom, Gal Chechik, Haggai Maron

Proceedings of the 38th International Conference on Machine Learning, 2021

Personalized Federated Learning using Hypernetworks.

Aviv Shamsian, Aviv Navon, Ethan Fetaya, Gal Chechik

Proceedings of the 38th International Conference on Machine Learning, 2021

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

Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, Ethan Fetaya

Proceedings of the 38th International Conference on Machine Learning, 2021

Learning the Pareto Front with Hypernetworks.

Aviv Navon, Aviv Shamsian, Ethan Fetaya, Gal Chechik

Proceedings of the 9th International Conference on Learning Representations, 2021

Auxiliary Learning by Implicit Differentiation.

Aviv Navon, Idan Achituve, Haggai Maron, Gal Chechik, Ethan Fetaya

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Restoration of fragmentary Babylonian texts using recurrent neural networks.

Ethan Fetaya, Yonatan Lifshitz, Elad Aaron, Shai Gordin

Proc. Natl. Acad. Sci. USA, 2020

Position-Agnostic Multi-Microphone Speech Dereverberation.

Yochai Yemini, Ethan Fetaya, Haggai Maron, Sharon Gannot

CoRR, 2020

On Size Generalization in Graph Neural Networks.

Gilad Yehudai, Ethan Fetaya, Eli A. Meirom, Gal Chechik, Haggai Maron

CoRR, 2020

On Learning Sets of Symmetric Elements.

Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya

Proceedings of the 37th International Conference on Machine Learning, 2020

Understanding the Limitations of Conditional Generative Models.

Ethan Fetaya, Jörn-Henrik Jacobsen, Will Grathwohl, Richard S. Zemel

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Conditional Generative Models are not Robust.

Ethan Fetaya, Jörn-Henrik Jacobsen, Richard S. Zemel

CoRR, 2019

Incremental Few-Shot Learning with Attention Attractor Networks.

Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

On the Universality of Invariant Networks.

Haggai Maron, Ethan Fetaya, Nimrod Segol, Yaron Lipman

Proceedings of the 36th International Conference on Machine Learning, 2019

Inference in Probabilistic Graphical Models by Graph Neural Networks.

KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard S. Zemel, Xaq Pitkow

Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019

2018

Neural Guided Constraint Logic Programming for Program Synthesis.

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

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Reviving and Improving Recurrent Back-Propagation.

Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard S. Zemel

Proceedings of the 35th International Conference on Machine Learning, 2018

Neural Relational Inference for Interacting Systems.

Thomas N. Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard S. Zemel

Proceedings of the 35th International Conference on Machine Learning, 2018

Leveraging Constraint Logic Programming for Neural Guided Program Synthesis.

Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Raquel Urtasun, Richard S. Zemel

Proceedings of the 6th International Conference on Learning Representations, 2018

Learning Discrete Weights Using the Local Reparameterization Trick.

Oran Shayer, Dan Levi, Ethan Fetaya

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Real-Time Category-Based and General Obstacle Detection for Autonomous Driving.

Noa Garnett, Shai Silberstein, Shaul Oron, Ethan Fetaya, Uri Verner, Ariel Ayash, Vlad Goldner, Rafi Cohen, Kobi Horn, Dan Levi

Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops, 2017

2016

Human Pose Estimation Using Deep Consensus Voting.

Ita Lifshitz, Ethan Fetaya, Shimon Ullman

Proceedings of the Computer Vision - ECCV 2016, 2016

Unsupervised Ensemble Learning with Dependent Classifiers.

Ariel Jaffe, Ethan Fetaya, Boaz Nadler, Tingting Jiang, Yuval Kluger

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Learning Local Invariant Mahalanobis Distances.

Ethan Fetaya, Shimon Ullman

Proceedings of the 32nd International Conference on Machine Learning, 2015

StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation.

Dan Levi, Noa Garnett, Ethan Fetaya

Proceedings of the British Machine Vision Conference 2015, 2015

Graph Approximation and Clustering on a Budget.

Ethan Fetaya, Ohad Shamir, Shimon Ullman

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2011

Homological Error Correcting Codes and Systolic Geometry

Ethan Fetaya

CoRR, 2011