Uri Shaham | DeepAI

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∙ 05/23/2023

ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding

We introduce ZeroSCROLLS, a zero-shot benchmark for natural language und...

0 Uri Shaham, et al. ∙

∙ 12/14/2022

Causes and Cures for Interference in Multilingual Translation

Multilingual machine translation models can benefit from synergy between...

0 Uri Shaham, et al. ∙

∙ 08/01/2022

Efficient Long-Text Understanding with Short-Text Models

Transformer-based pretrained language models (LMs) are ubiquitous across...

0 Maor Ivgi, et al. ∙

∙ 05/22/2022

Instruction Induction: From Few Examples to Natural Language Task Descriptions

Large language models are able to perform a task by conditioning on a fe...

0 Or Honovich, et al. ∙

∙ 01/10/2022

SCROLLS: Standardized CompaRison Over Long Language Sequences

NLP benchmarks have largely focused on short texts, such as sentences an...

0 Uri Shaham, et al. ∙

∙ 10/12/2021

Single Independent Component Recovery and Applications

Latent variable discovery is a central problem in data analysis with a b...

0 Uri Shaham, et al. ∙

∙ 10/11/2021

Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features

Modern datasets often contain large subsets of correlated features and n...

0 Uri Shaham, et al. ∙

∙ 07/20/2021

What Do You Get When You Cross Beam Search with Nucleus Sampling?

We combine beam search with the probabilistic pruning technique of nucle...

0 Uri Shaham, et al. ∙

∙ 03/01/2021

Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language

Current NLP datasets targeting ambiguity can be solved by a native speak...

0 Avia Efrat, et al. ∙

∙ 11/15/2020

Deep Ordinal Regression using Optimal Transport Loss and Unimodal Output Probabilities

We propose a framework for deep ordinal regression, based on unimodal ou...

0 Uri Shaham, et al. ∙

∙ 08/21/2020

Neural Machine Translation without Embeddings

Many NLP models follow the embed-contextualize-predict paradigm, in whic...

0 Uri Shaham, et al. ∙

∙ 07/09/2020

Let the Data Choose its Features: Differentiable Unsupervised Feature Selection

Scientific observations often consist of a large number of variables (fe...

7 Ofir Lindenbaum, et al. ∙

∙ 03/31/2020

Learning to Ask Medical Questions using Reinforcement Learning

We propose a novel reinforcement learning-based approach for adaptive an...

2 Uri Shaham, et al. ∙

∙ 07/19/2018

Automated Characterization of Stenosis in Invasive Coronary Angiography Images with Convolutional Neural Networks

The determination of a coronary stenosis and its severity in current cli...

0 Benjamin Au, et al. ∙

∙ 03/28/2018

Defending against Adversarial Images using Basis Functions Transformations

We study the effectiveness of various approaches that defend against adv...

0 Uri Shaham, et al. ∙

∙ 01/04/2018

SpectralNet: Spectral Clustering using Deep Neural Networks

Spectral clustering is a leading and popular technique in unsupervised d...

0 Uri Shaham, et al. ∙

∙ 02/09/2017

Stochastic Neighbor Embedding separates well-separated clusters

Stochastic Neighbor Embedding and its variants are widely used dimension...

0 Uri Shaham, et al. ∙

∙ 10/13/2016

Removal of Batch Effects using Distribution-Matching Residual Networks

Sources of variability in experimentally derived data include measuremen...

0 Uri Shaham, et al. ∙

∙ 06/02/2016

DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network

Medical practitioners use survival models to explore and understand the ...

0 Jared Katzman, et al. ∙

∙ 02/06/2016

A Deep Learning Approach to Unsupervised Ensemble Learning

We show how deep learning methods can be applied in the context of crowd...

0 Uri Shaham, et al. ∙

∙ 12/29/2015

Common Variable Learning and Invariant Representation Learning using Siamese Neural Networks

We consider the statistical problem of learning common source of variabi...

0 Uri Shaham, et al. ∙

∙ 11/17/2015

Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization

We propose a general framework for increasing local stability of Artific...

0 Uri Shaham, et al. ∙

∙ 09/24/2015

Provable approximation properties for deep neural networks

We discuss approximation of functions using deep neural nets. Given a fu...

0 Uri Shaham, et al. ∙

∙ 06/25/2015

Diffusion Nets

Non-linear manifold learning enables high-dimensional data analysis, but...

0 Gal Mishne, et al. ∙

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