b'Ethan Fetaya'

research

∙ 07/04/2023

Learning Discrete Weights and Activations Using the Local Reparameterization Trick

In computer vision and machine learning, a crucial challenge is to lower...

0 Guy Berger, et al. ∙

research

∙ 06/19/2023

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

The field of generative models has recently witnessed significant progre...

0 Ariel Lapid, et al. ∙

research

∙ 06/05/2023

LipVoicer: Generating Speech from Silent Videos Guided by Lip Reading

Lip-to-speech involves generating a natural-sounding speech synchronized...

0 Yochai Yemini, et al. ∙

research

∙ 05/30/2023

DisCLIP: Open-Vocabulary Referring Expression Generation

Referring Expressions Generation (REG) aims to produce textual descripti...

0 Lior Bracha, et al. ∙

research

∙ 02/19/2023

Guided Deep Kernel Learning

Combining Gaussian processes with the expressive power of deep neural ne...

0 Idan Achituve, et al. ∙

research

∙ 01/31/2023

Auxiliary Learning as an Asymmetric Bargaining Game

Auxiliary learning is an effective method for enhancing the generalizati...

0 Aviv Shamsian, et al. ∙

research

∙ 01/30/2023

Equivariant Architectures for Learning in Deep Weight Spaces

Designing machine learning architectures for processing neural networks ...

0 Aviv Navon, et al. ∙

research

∙ 09/04/2022

Communication Efficient Distributed Learning over Wireless Channels

Vertical distributed learning exploits the local features collected by m...

0 Idan Achituve, et al. ∙

research

∙ 06/22/2022

A Study on the Evaluation of Generative Models

Implicit generative models, which do not return likelihood values, such ...

0 Eyal Betzalel, et al. ∙

research

∙ 06/05/2022

Functional Ensemble Distillation

Bayesian models have many desirable properties, most notable is their ab...

0 Coby Penso, et al. ∙

research

∙ 02/02/2022

Multi-Task Learning as a Bargaining Game

In Multi-task learning (MTL), a joint model is trained to simultaneously...

0 Aviv Navon, et al. ∙

research

∙ 10/11/2021

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

Bayesian learning via Stochastic Gradient Langevin Dynamics (SGLD) has b...

0 Guy Heller, et al. ∙

research

∙ 06/29/2021

Personalized Federated Learning with Gaussian Processes

Federated learning aims to learn a global model that performs well on cl...

0 Idan Achituve, et al. ∙

research

∙ 03/08/2021

Personalized Federated Learning using Hypernetworks

Personalized federated learning is tasked with training machine learning...

0 Aviv Shamsian, et al. ∙

research

∙ 02/15/2021

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

Gaussian processes (GPs) are non-parametric, flexible, models that work ...

0 Idan Achituve, et al. ∙

research

∙ 10/22/2020

Position-Agnostic Multi-Microphone Speech Dereverberation

Neural networks (NNs) have been widely applied in speech processing task...

0 Yochai Yemini, et al. ∙

research

∙ 10/17/2020

On Size Generalization in Graph Neural Networks

Graph neural networks (GNNs) can process graphs of different sizes but t...

0 Gilad Yehudai, et al. ∙

research

∙ 10/08/2020

Learning the Pareto Front with Hypernetworks

Multi-objective optimization problems are prevalent in machine learning....

0 Aviv Navon, et al. ∙

research

∙ 03/04/2020

Restoration of Fragmentary Babylonian Texts Using Recurrent Neural Networks

The main source of information regarding ancient Mesopotamian history an...

0 Ethan Fetaya, et al. ∙

research

∙ 02/20/2020

On Learning Sets of Symmetric Elements

Learning from unordered sets is a fundamental learning setup, which is a...

21 Haggai Maron, et al. ∙

research

∙ 06/04/2019

Conditional Generative Models are not Robust

Class-conditional generative models are an increasingly popular approach...

0 Ethan Fetaya, et al. ∙

research

∙ 05/28/2019

Evaluating and Calibrating Uncertainty Prediction in Regression Tasks

Predicting not only the target but also an accurate measure of uncertain...

0 Dan Levi, et al. ∙

research

∙ 01/27/2019

On the Universality of Invariant Networks

Constraining linear layers in neural networks to respect symmetry transf...

0 Haggai Maron, et al. ∙

research

∙ 10/16/2018

Incremental Few-Shot Learning with Attention Attractor Networks

Machine learning classifiers are often trained to recognize a set of pre...

0 Mengye Ren, et al. ∙

research

∙ 09/08/2018

Neural Guided Constraint Logic Programming for Program Synthesis

Synthesizing programs using example input/outputs is a classic problem i...

0 Lisa Zhang, et al. ∙

research

∙ 03/21/2018

Inference in Probabilistic Graphical Models by Graph Neural Networks

A useful computation when acting in a complex environment is to infer th...

0 KiJung Yoon, et al. ∙

research

∙ 03/16/2018

Reviving and Improving Recurrent Back-Propagation

In this paper, we revisit the recurrent back-propagation (RBP) algorithm...

0 Renjie Liao, et al. ∙

research

∙ 02/13/2018

Neural Relational Inference for Interacting Systems

Interacting systems are prevalent in nature, from dynamical systems in p...

0 Thomas Kipf, et al. ∙

research

∙ 10/21/2017

Learning Discrete Weights Using the Local Reparameterization Trick

Recent breakthroughs in computer vision make use of large deep neural ne...

0 Oran Shayer, et al. ∙

research

∙ 03/27/2016

Human Pose Estimation using Deep Consensus Voting

In this paper we consider the problem of human pose estimation from a si...

0 Ita Lifshitz, et al. ∙

research

∙ 10/20/2015

Unsupervised Ensemble Learning with Dependent Classifiers

In unsupervised ensemble learning, one obtains predictions from multiple...

0 Ariel Jaffe, et al. ∙

research

∙ 02/04/2015

Learning Local Invariant Mahalanobis Distances

For many tasks and data types, there are natural transformations to whic...

0 Ethan Fetaya, et al. ∙

research

∙ 06/10/2014

Graph Approximation and Clustering on a Budget

We consider the problem of learning from a similarity matrix (such as sp...

0 Ethan Fetaya, et al. ∙

Ethan Fetaya

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