Search Results for author: Ethan Fetaya

Found 22 papers, 11 papers with code

Personalized Federated Learning with Gaussian Processes

1 code implementation29 Jun 2021 Idan Achituve, Aviv Shamsian, Aviv Navon, Gal Chechik, Ethan Fetaya

A key challenge in this setting is to learn effectively across clients even though each client has unique data that is often limited in size.

 Ranked #1 on Personalized Federated Learning on CIFAR-10 (ACC@1-100Clients metric)

Gaussian Processes Personalized Federated Learning

Personalized Federated Learning using Hypernetworks

1 code implementation8 Mar 2021 Aviv Shamsian, Aviv Navon, Ethan Fetaya, Gal Chechik

In this approach, a central hypernetwork model is trained to generate a set of models, one model for each client.

Personalized Federated Learning

Scene-Agnostic Multi-Microphone Speech Dereverberation

no code implementations22 Oct 2020 Yochai Yemini, Ethan Fetaya, Haggai Maron, Sharon Gannot

We use noisy and noiseless versions of a simulated reverberant dataset to test the proposed architecture.

Speech Dereverberation

From Local Structures to Size Generalization in Graph Neural Networks

no code implementations17 Oct 2020 Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik, Haggai Maron

In this paper, we identify an important type of data where generalization from small to large graphs is challenging: graph distributions for which the local structure depends on the graph size.

Combinatorial Optimization Domain Adaptation +2

Learning the Pareto Front with Hypernetworks

1 code implementation ICLR 2021 Aviv Navon, Aviv Shamsian, Gal Chechik, Ethan Fetaya

Here, we tackle the problem of learning the entire Pareto front, with the capability of selecting a desired operating point on the front after training.

Fairness Multiobjective Optimization +3

Auxiliary Learning by Implicit Differentiation

1 code implementation ICLR 2021 Aviv Navon, Idan Achituve, Haggai Maron, Gal Chechik, Ethan Fetaya

Two main challenges arise in this multi-task learning setting: (i) designing useful auxiliary tasks; and (ii) combining auxiliary tasks into a single coherent loss.

Auxiliary Learning Multi-Task Learning +2

Restoration of Fragmentary Babylonian Texts Using Recurrent Neural Networks

no code implementations4 Mar 2020 Ethan Fetaya, Yonatan Lifshitz, Elad Aaron, Shai Gordin

The main source of information regarding ancient Mesopotamian history and culture are clay cuneiform tablets.

On Learning Sets of Symmetric Elements

2 code implementations ICML 2020 Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya

We first characterize the space of linear layers that are equivariant both to element reordering and to the inherent symmetries of elements, like translation in the case of images.

3D Shape Recognition Deblurring

Evaluating and Calibrating Uncertainty Prediction in Regression Tasks

no code implementations28 May 2019 Dan Levi, Liran Gispan, Niv Giladi, Ethan Fetaya

Predicting not only the target but also an accurate measure of uncertainty is important for many machine learning applications and in particular safety-critical ones.

Object Detection

On the Universality of Invariant Networks

no code implementations27 Jan 2019 Haggai Maron, Ethan Fetaya, Nimrod Segol, Yaron Lipman

We conclude the paper by proving a necessary condition for the universality of $G$-invariant networks that incorporate only first-order tensors.

Incremental Few-Shot Learning with Attention Attractor Networks

1 code implementation NeurIPS 2019 Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel

This paper addresses this problem, incremental few-shot learning, where a regular classification network has already been trained to recognize a set of base classes, and several extra novel classes are being considered, each with only a few labeled examples.

Few-Shot Learning General Classification

Inference in Probabilistic Graphical Models by Graph Neural Networks

1 code implementation21 Mar 2018 KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard Zemel, Xaq Pitkow

Message-passing algorithms, such as belief propagation, are a natural way to disseminate evidence amongst correlated variables while exploiting the graph structure, but these algorithms can struggle when the conditional dependency graphs contain loops.

Decision Making

Reviving and Improving Recurrent Back-Propagation

1 code implementation ICML 2018 Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard Zemel

We examine all RBP variants along with BPTT and TBPTT in three different application domains: associative memory with continuous Hopfield networks, document classification in citation networks using graph neural networks and hyperparameter optimization for fully connected networks.

Document Classification Hyperparameter Optimization

Neural Relational Inference for Interacting Systems

8 code implementations ICML 2018 Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel

Interacting systems are prevalent in nature, from dynamical systems in physics to complex societal dynamics.

Motion Capture

Learning Discrete Weights Using the Local Reparameterization Trick

no code implementations ICLR 2018 Oran Shayer, Dan Levi, Ethan Fetaya

We show how a simple modification to the local reparameterization trick, previously used to train Gaussian distributed weights, enables the training of discrete weights.

Human Pose Estimation using Deep Consensus Voting

no code implementations27 Mar 2016 Ita Lifshitz, Ethan Fetaya, Shimon Ullman

In this paper we consider the problem of human pose estimation from a single still image.

Pose Estimation

Unsupervised Ensemble Learning with Dependent Classifiers

no code implementations20 Oct 2015 Ariel Jaffe, Ethan Fetaya, Boaz Nadler, Tingting Jiang, Yuval Kluger

In unsupervised ensemble learning, one obtains predictions from multiple sources or classifiers, yet without knowing the reliability and expertise of each source, and with no labeled data to assess it.

Ensemble Learning

Learning Local Invariant Mahalanobis Distances

no code implementations4 Feb 2015 Ethan Fetaya, Shimon Ullman

For many tasks and data types, there are natural transformations to which the data should be invariant or insensitive.

Graph Approximation and Clustering on a Budget

no code implementations10 Jun 2014 Ethan Fetaya, Ohad Shamir, Shimon Ullman

We consider the problem of learning from a similarity matrix (such as spectral clustering and lowd imensional embedding), when computing pairwise similarities are costly, and only a limited number of entries can be observed.

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