Hebrew University of Jerusalem

research

∙ 11/25/2020

Multiclass non-Adversarial Image Synthesis, with Application to Classification from Very Small Sample

The generation of synthetic images is currently being dominated by Gener...

Itamar Winter, et al. ∙

research

∙ 02/13/2020

Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization

Domain generalization is the problem of machine learning when the traini...

Vikas K. Garg, et al. ∙

research

∙ 08/19/2021

Topo2vec: Topography Embedding Using the Fractal Effect

Recent advances in deep learning have transformed many fields by introdu...

Jonathan Kavitzky, et al. ∙

research

∙ 10/31/2018

An Information-Theoretic Framework for Non-linear Canonical Correlation Analysis

Canonical Correlation Analysis (CCA) is a linear representation learning...

Amichai Painsky, et al. ∙

research

∙ 03/10/2022

An Empirical Investigation of 3D Anomaly Detection and Segmentation

Anomaly detection and segmentation in images has made tremendous progres...

Eliahu Horwitz, et al. ∙

research

∙ 04/26/2019

Learning to Prune: Speeding up Repeated Computations

It is common to encounter situations where one must solve a sequence of ...

Daniel Alabi, et al. ∙

research

∙ 02/17/2020

Causal Feature Discovery through Strategic Modification

We consider an online regression setting in which individuals adapt to t...

Yahav Bechavod, et al. ∙

research

∙ 09/13/2021

Image Shape Manipulation from a Single Augmented Training Sample

In this paper, we present DeepSIM, a generative model for conditional im...

Yael Vinker, et al. ∙

research

∙ 02/09/2022

Scenario-Assisted Deep Reinforcement Learning

Deep reinforcement learning has proven remarkably useful in training age...

Raz Yerushalmi, et al. ∙

research

∙ 09/22/2021

Cross-linguistically Consistent Semantic and Syntactic Annotation of Child-directed Speech

While corpora of child speech and child-directed speech (CDS) have enabl...

Ida Szubert, et al. ∙

research

∙ 06/09/2022

A Learning-Theoretic Framework for Certified Auditing of Machine Learning Models

Responsible use of machine learning requires that models be audited for ...

Chhavi Yadav, et al. ∙

research

∙ 02/09/2020

On the Complexity of Minimizing Convex Finite Sums Without Using the Indices of the Individual Functions

Recent advances in randomized incremental methods for minimizing L-smoot...

Yossi Arjevani, et al. ∙

research

∙ 05/30/2018

Why do deep convolutional networks generalize so poorly to small image transformations?

Deep convolutional network architectures are often assumed to guarantee ...

Aharon Azulay, et al. ∙

research

∙ 02/06/2019

Equal Opportunity in Online Classification with Partial Feedback

We study an online classification problem with partial feedback in which...

Yahav Bechavod, et al. ∙

research

∙ 06/22/2022

A Systematic Comparison of Phonetic Aware Techniques for Speech Enhancement

Speech enhancement has seen great improvement in recent years using end-...

Or Tal, et al. ∙

research

∙ 05/23/2018

On self-play computation of equilibrium in poker

We compare performance of the genetic algorithm and the counterfactual r...

Mikhail Goykhman, et al. ∙

research

∙ 05/31/2018

On GANs and GMMs

A longstanding problem in machine learning is to find unsupervised metho...

Eitan Richardson, et al. ∙

research

∙ 02/18/2020

Learning Parities with Neural Networks

In recent years we see a rapidly growing line of research which shows le...

Amit Daniely, et al. ∙

research

∙ 08/23/2021

ComSum: Commit Messages Summarization and Meaning Preservation

We present ComSum, a data set of 7 million commit messages for text summ...

Leshem Choshen, et al. ∙

research

∙ 09/13/2021

The Grammar-Learning Trajectories of Neural Language Models

The learning trajectories of linguistic phenomena provide insight into t...

Leshem Choshen, et al. ∙

research

∙ 07/25/2022

WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models

While vision-and-language models perform well on tasks such as visual qu...

Yonatan Bitton, et al. ∙

research

∙ 12/13/2017

Ballpark Crowdsourcing: The Wisdom of Rough Group Comparisons

Crowdsourcing has become a popular method for collecting labeled trainin...

Tom Hope, et al. ∙

research

∙ 11/16/2017

On evolutionary selection of blackjack strategies

We apply the approach of evolutionary programming to the problem of opti...

Mikhail Goykhman, et al. ∙

research

∙ 10/25/2017

Benefits of Depth for Long-Term Memory of Recurrent Networks

The key attribute that drives the unprecedented success of modern Recurr...

Yoav Levine, et al. ∙

research

∙ 10/12/2017

Sum-Product-Quotient Networks

We present a novel tractable generative model that extends Sum-Product N...

Or Sharir, et al. ∙

research

∙ 11/22/2017

Neuron-level Selective Context Aggregation for Scene Segmentation

Contextual information provides important cues for disambiguating visual...

Zhenhua Wang, et al. ∙

research

∙ 11/07/2017

Gaussian Lower Bound for the Information Bottleneck Limit

The Information Bottleneck (IB) is a conceptual method for extracting th...

Amichai Painsky, et al. ∙

research

∙ 05/05/2017

Analysis and Design of Convolutional Networks via Hierarchical Tensor Decompositions

The driving force behind convolutional networks - the most successful de...

Nadav Cohen, et al. ∙

research

∙ 04/05/2017

Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design

Deep convolutional networks have witnessed unprecedented success in vari...

Yoav Levine, et al. ∙

research

∙ 10/26/2017

Optimal Shrinkage of Singular Values Under Random Data Contamination

A low rank matrix X has been contaminated by uniformly distributed noise...

Danny Barash, et al. ∙

research

∙ 03/06/2017

On the Expressive Power of Overlapping Architectures of Deep Learning

Expressive efficiency refers to the relation between two architectures A...

Or Sharir, et al. ∙

research

∙ 10/13/2016

Tractable Generative Convolutional Arithmetic Circuits

Casting neural networks in generative frameworks is a highly sought-afte...

Or Sharir, et al. ∙

research

∙ 06/17/2017

Accelerating Innovation Through Analogy Mining

The availability of large idea repositories (e.g., the U.S. patent datab...

Tom Hope, et al. ∙

research

∙ 05/22/2016

Inductive Bias of Deep Convolutional Networks through Pooling Geometry

Our formal understanding of the inductive bias that drives the success o...

Nadav Cohen, et al. ∙

research

∙ 03/01/2016

Convolutional Rectifier Networks as Generalized Tensor Decompositions

Convolutional rectifier networks, i.e. convolutional neural networks wit...

Nadav Cohen, et al. ∙

research

∙ 12/06/2015

Linear Readout of Object Manifolds

Objects are represented in sensory systems by continuous manifolds due t...

SueYeon Chung, et al. ∙

research

∙ 09/16/2015

On the Expressive Power of Deep Learning: A Tensor Analysis

It has long been conjectured that hypotheses spaces suitable for data th...

Nadav Cohen, et al. ∙

research

∙ 06/09/2015

Deep SimNets

We present a deep layered architecture that generalizes convolutional ne...

Nadav Cohen, et al. ∙

research

∙ 05/28/2017

Learning Data Manifolds with a Cutting Plane Method

We consider the problem of classifying data manifolds where each manifol...

SueYeon Chung, et al. ∙

research

∙ 06/24/2016

An Axiomatic Approach to Routing

Information delivery in a network of agents is a key issue for large, co...

Omer Lev, et al. ∙

research

∙ 04/13/2016

Strategyproof Peer Selection using Randomization, Partitioning, and Apportionment

Peer review, evaluation, and selection is a fundamental aspect of modern...

Haris Aziz, et al. ∙

research

∙ 02/20/2002

Covariance Plasticity and Regulated Criticality

We propose that a regulation mechanism based on Hebbian covariance plast...

Elie Bienenstock, et al. ∙

research

∙ 12/21/2015

Information-Theoretic Bounded Rationality

Bounded rationality, that is, decision-making and planning under resourc...

Pedro A. Ortega, et al. ∙

research

∙ 08/18/2016

A Tight Convex Upper Bound on the Likelihood of a Finite Mixture

The likelihood function of a finite mixture model is a non-convex functi...

Elad Mezuman, et al. ∙

research

∙ 07/24/2016

Online Trajectory Segmentation and Summary With Applications to Visualization and Retrieval

Trajectory segmentation is the process of subdividing a trajectory into ...

Yehezkel S. Resheff, et al. ∙

research

∙ 06/30/2016

Ballpark Learning: Estimating Labels from Rough Group Comparisons

We are interested in estimating individual labels given only coarse, agg...

Tom Hope, et al. ∙

research

∙ 06/16/2016

Estimating mutual information in high dimensions via classification error

Multivariate pattern analyses approaches in neuroimaging are fundamental...

Charles Y. Zheng, et al. ∙

research

∙ 06/16/2016

How many faces can be recognized? Performance extrapolation for multi-class classification

The difficulty of multi-class classification generally increases with th...

Charles Y. Zheng, et al. ∙

research

∙ 04/18/2016

Memory shapes time perception and intertemporal choices

There is a consensus that human and non-human subjects experience tempor...

Pedro A. Ortega, et al. ∙

research

∙ 11/17/2015

Optimized Linear Imputation

Often in real-world datasets, especially in high dimensional data, some ...

Yehezkel S. Resheff, et al. ∙

Hebrew University of Jerusalem

Featured Researchers