Uri Shaham

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Yale University

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

We combine beam search with the probabilistic pruning technique of nucle...
07/20/2021 ∙ by Uri Shaham, et al. ∙ 0 ∙ share

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Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language

Current NLP datasets targeting ambiguity can be solved by a native speak...
03/01/2021 ∙ by Avia Efrat, et al. ∙ 0 ∙ share

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Deep Ordinal Regression using Optimal Transport Loss and Unimodal Output Probabilities

We propose a framework for deep ordinal regression, based on unimodal ou...
11/15/2020 ∙ by Uri Shaham, et al. ∙ 0 ∙ share

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Neural Machine Translation without Embeddings

Many NLP models follow the embed-contextualize-predict paradigm, in whic...
08/21/2020 ∙ by Uri Shaham, et al. ∙ 0 ∙ share

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Let the Data Choose its Features: Differentiable Unsupervised Feature Selection

Scientific observations often consist of a large number of variables (fe...
07/09/2020 ∙ by Ofir Lindenbaum, et al. ∙ 7 ∙ share

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Learning to Ask Medical Questions using Reinforcement Learning

We propose a novel reinforcement learning-based approach for adaptive an...
03/31/2020 ∙ by Uri Shaham, et al. ∙ 2 ∙ share

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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...
07/19/2018 ∙ by Benjamin Au, et al. ∙ 0 ∙ share

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Defending against Adversarial Images using Basis Functions Transformations

We study the effectiveness of various approaches that defend against adv...
03/28/2018 ∙ by Uri Shaham, et al. ∙ 0 ∙ share

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SpectralNet: Spectral Clustering using Deep Neural Networks

Spectral clustering is a leading and popular technique in unsupervised d...
01/04/2018 ∙ by Uri Shaham, et al. ∙ 0 ∙ share

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Stochastic Neighbor Embedding separates well-separated clusters

Stochastic Neighbor Embedding and its variants are widely used dimension...
02/09/2017 ∙ by Uri Shaham, et al. ∙ 0 ∙ share

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Removal of Batch Effects using Distribution-Matching Residual Networks

Sources of variability in experimentally derived data include measuremen...
10/13/2016 ∙ by Uri Shaham, et al. ∙ 0 ∙ share

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

Medical practitioners use survival models to explore and understand the ...
06/02/2016 ∙ by Jared Katzman, et al. ∙ 0 ∙ share

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A Deep Learning Approach to Unsupervised Ensemble Learning

We show how deep learning methods can be applied in the context of crowd...
02/06/2016 ∙ by Uri Shaham, et al. ∙ 0 ∙ share

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Common Variable Learning and Invariant Representation Learning using Siamese Neural Networks

We consider the statistical problem of learning common source of variabi...
12/29/2015 ∙ by Uri Shaham, et al. ∙ 0 ∙ share

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

We propose a general framework for increasing local stability of Artific...
11/17/2015 ∙ by Uri Shaham, et al. ∙ 0 ∙ share

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Provable approximation properties for deep neural networks

We discuss approximation of functions using deep neural nets. Given a fu...
09/24/2015 ∙ by Uri Shaham, et al. ∙ 0 ∙ share

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Diffusion Nets

Non-linear manifold learning enables high-dimensional data analysis, but...
06/25/2015 ∙ by Gal Mishne, et al. ∙ 0 ∙ share

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Uri Shaham

Featured Co-authors

Jun Zhao

Omer Levy

Stefan Steinerberger

Ronen Basri

Yuval Kluger

Tom Zahavy

Xiuyuan Cheng

Boaz Nadler

Alexander Cloninger

Tingting Jiang

Gal Mishne

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

Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language

Deep Ordinal Regression using Optimal Transport Loss and Unimodal Output Probabilities

Neural Machine Translation without Embeddings

Let the Data Choose its Features: Differentiable Unsupervised Feature Selection

Learning to Ask Medical Questions using Reinforcement Learning

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

Defending against Adversarial Images using Basis Functions Transformations

SpectralNet: Spectral Clustering using Deep Neural Networks

Stochastic Neighbor Embedding separates well-separated clusters

Removal of Batch Effects using Distribution-Matching Residual Networks

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

A Deep Learning Approach to Unsupervised Ensemble Learning

Common Variable Learning and Invariant Representation Learning using Siamese Neural Networks

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

Provable approximation properties for deep neural networks

Diffusion Nets