Homological Error Correcting Codes and Systolic Geometry - Ethan Fetaya (CoRR, 2011)

Graph Approximation and Clustering on a Budget. - Ethan Fetaya; Ohad Shamir; Shimon Ullman (AISTATS, 2015)

StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation. - Dan Levi; Noa Garnett; Ethan Fetaya (BMVC, 2015)

Learning Local Invariant Mahalanobis Distances. - Ethan Fetaya; Shimon Ullman (ICML, 2015)

Unsupervised Ensemble Learning with Dependent Classifiers. - Ariel Jaffe; Ethan Fetaya; Boaz Nadler; Tingting Jiang; Yuval Kluger (AISTATS, 2016)

Human Pose Estimation Using Deep Consensus Voting. - Ita Lifshitz; Ethan Fetaya; Shimon Ullman (ECCV (2), 2016)

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 (ICCV Workshops, 2017)

Learning Discrete Weights Using the Local Reparameterization Trick. - Oran Shayer; Dan Levi; Ethan Fetaya (ICLR (Poster), 2018)

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 (ICLR (Workshop), 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 (ICLR (Workshop), 2018)

Neural Relational Inference for Interacting Systems. - Thomas N. Kipf; Ethan Fetaya; Kuan-Chieh Wang; Max Welling; Richard S. Zemel (ICML, 2018)

Reviving and Improving Recurrent Back-Propagation. - Renjie Liao; Yuwen Xiong; Ethan Fetaya; Lisa Zhang; KiJung Yoon; Xaq Pitkow; Raquel Urtasun; Richard S. Zemel (ICML, 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 (NeurIPS, 2018)

On the Universality of Invariant Networks. - Haggai Maron; Ethan Fetaya; Nimrod Segol; Yaron Lipman (ICML, 2019)

Incremental Few-Shot Learning with Attention Attractor Networks. - Mengye Ren; Renjie Liao; Ethan Fetaya; Richard S. Zemel (NeurIPS, 2019)

Conditional Generative Models are not Robust. - Ethan Fetaya; Jörn-Henrik Jacobsen; Richard S. Zemel (CoRR, 2019)

Understanding the Limitations of Conditional Generative Models. - Ethan Fetaya; Jörn-Henrik Jacobsen; Will Grathwohl; Richard S. Zemel (ICLR, 2020)

On Learning Sets of Symmetric Elements. - Haggai Maron; Or Litany; Gal Chechik; Ethan Fetaya (ICML, 2020)

On Size Generalization in Graph Neural Networks. - Gilad Yehudai; Ethan Fetaya; Eli A. Meirom; Gal Chechik; Haggai Maron (CoRR, 2020)

Position-Agnostic Multi-Microphone Speech Dereverberation. - Yochai Yemini; Ethan Fetaya; Haggai Maron; Sharon Gannot (CoRR, 2020)

Restoration of fragmentary Babylonian texts using recurrent neural networks. - Ethan Fetaya; Yonatan Lifshitz; Elad Aaron; Shai Gordin (Proc. Natl. Acad. Sci. USA, 2020)

Auxiliary Learning by Implicit Differentiation. - Aviv Navon; Idan Achituve; Haggai Maron; Gal Chechik; Ethan Fetaya (ICLR, 2021)

Learning the Pareto Front with Hypernetworks. - Aviv Navon; Aviv Shamsian; Ethan Fetaya; Gal Chechik (ICLR, 2021)

GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning. - Idan Achituve; Aviv Navon; Yochai Yemini; Gal Chechik; Ethan Fetaya (ICML, 2021)

Personalized Federated Learning using Hypernetworks. - Aviv Shamsian; Aviv Navon; Ethan Fetaya; Gal Chechik (ICML, 2021)

From Local Structures to Size Generalization in Graph Neural Networks. - Gilad Yehudai; Ethan Fetaya; Eli A. Meirom; Gal Chechik; Haggai Maron (ICML, 2021)

On Learning Sets of Symmetric Elements (Extended Abstract). - Haggai Maron; Or Litany; Gal Chechik; Ethan Fetaya (IJCAI, 2021)

Scene-Agnostic Multi-Microphone Speech Dereverberation. - Yochai Yemini; Ethan Fetaya; Haggai Maron; Sharon Gannot (Interspeech, 2021)

Personalized Federated Learning With Gaussian Processes. - Idan Achituve; Aviv Shamsian; Aviv Navon; Gal Chechik; Ethan Fetaya (NeurIPS, 2021)

Multi-Task Learning as a Bargaining Game. - Aviv Navon; Aviv Shamsian; Idan Achituve; Haggai Maron; Kenji Kawaguchi; Gal Chechik; Ethan Fetaya (ICML, 2022)

Functional Ensemble Distillation. - Coby Penso; Idan Achituve; Ethan Fetaya (NeurIPS, 2022)

A Study on the Evaluation of Generative Models. - Eyal Betzalel; Coby Penso; Aviv Navon; Ethan Fetaya (CoRR, 2022)

Evaluating and Calibrating Uncertainty Prediction in Regression Tasks. - Dan Levi; Liran Gispan; Niv Giladi; Ethan Fetaya (Sensors, 2022)

DisCLIP: Open-Vocabulary Referring Expression Generation. - Lior Bracha; Eitan Shaar; Aviv Shamsian; Ethan Fetaya; Gal Chechik (BMVC, 2023)

Object-Centric Open-Vocabulary Image Retrieval with Aggregated Features. - Hila Levi; Guy Heller; Dan Levi; Ethan Fetaya (BMVC, 2023)

Equivariant Architectures for Learning in Deep Weight Spaces. - Aviv Navon; Aviv Shamsian; Idan Achituve; Ethan Fetaya; Gal Chechik; Haggai Maron (ICML, 2023)

Auxiliary Learning as an Asymmetric Bargaining Game. - Aviv Shamsian; Aviv Navon; Neta Glazer; Kenji Kawaguchi; Gal Chechik; Ethan Fetaya (ICML, 2023)

Can Stochastic Gradient Langevin Dynamics Provide Differential Privacy for Deep Learning? - Guy Heller; Ethan Fetaya (SaTML, 2023)

Guided Deep Kernel Learning. - Idan Achituve; Gal Chechik; Ethan Fetaya (UAI, 2023)

GD-VDM: Generated Depth for better Diffusion-based Video Generation. - Ariel Lapid; Idan Achituve; Lior Bracha; Ethan Fetaya (CoRR, 2023)

Learning Discrete Weights and Activations Using the Local Reparameterization Trick. - Guy Berger; Aviv Navon; Ethan Fetaya (CoRR, 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; et al. (CoRR, 2023)

Communication Efficient Distributed Learning Over Wireless Channels. - Idan Achituve; Wenbo Wang 004; Ethan Fetaya; Amir Leshem (IEEE Signal Process. Lett., 2023)

LipVoicer: Generating Speech from Silent Videos Guided by Lip Reading. - Yochai Yemini; Aviv Shamsian; Lior Bracha; Sharon Gannot; Ethan Fetaya (ICLR, 2024)

Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning. - Idan Achituve; Idit Diamant; Arnon Netzer; Gal Chechik; Ethan Fetaya (ICML, 2024)

Equivariant Deep Weight Space Alignment. - Aviv Navon; Aviv Shamsian; Ethan Fetaya; Gal Chechik; Nadav Dym; Haggai Maron (ICML, 2024)

Improved Generalization of Weight Space Networks via Augmentations. - Aviv Shamsian; Aviv Navon; David W. Zhang; Yan Zhang; Ethan Fetaya; Gal Chechik; Haggai Maron (ICML, 2024)

Low-Latency Single-Microphone Speaker Separation with Temporal Convolutional Networks Using Speaker Representations. - Boris Rubenchik; Elior Hadad; Eli Tzirkel; Ethan Fetaya; Sharon Gannot (IWAENC, 2024)

Multi Task Inverse Reinforcement Learning for Common Sense Reward. - Neta Glazer; Aviv Navon; Aviv Shamsian; Ethan Fetaya (CoRR, 2024)

Evaluation Metrics for Generative Models: An Empirical Study. - Eyal Betzalel; Coby Penso; Ethan Fetaya (Mach. Learn. Knowl. Extr., 2024)

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels. - Coby Penso; Jacob Goldberger; Ethan Fetaya (COPA, 2025)

Estimating the Conformal Prediction Threshold from Noisy Labels. - Coby Penso; Jacob Goldberger; Ethan Fetaya (CoRR, 2025)

Inverse Problem Sampling in Latent Space Using Sequential Monte Carlo. - Idan Achituve; Hai Victor Habi; Amir Rosenfeld; Arnon Netzer; Idit Diamant; Ethan Fetaya (CoRR, 2025)

Adversarial Robustness in Parameter-Space Classifiers. - Tamir Shor; Ethan Fetaya; Chaim Baskin; Alexander M. Bronstein (CoRR, 2025)

Go Beyond Your Means: Unlearning with Per-Sample Gradient Orthogonalization. - Aviv Shamsian; Eitan Shaar; Aviv Navon; Gal Chechik; Ethan Fetaya (CoRR, 2025)

Few-Shot Speech Deepfake Detection Adaptation with Gaussian Processes. - Neta Glazer; David Chernin; Idan Achituve; Sharon Gannot; Ethan Fetaya (CoRR, 2025)