Uri Shaham

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Yale University

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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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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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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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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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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Uri Shaham

Featured Co-authors

Jun Zhao

Ronen Basri

Stefan Steinerberger

Tom Zahavy

Yuval Kluger

Boaz Nadler

Xiuyuan Cheng

Alexander Cloninger

Sahand Negahban

Gal Mishne

Ronald R. Coifman

Learning to Ask Medical Questions using Reinforcement Learning

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

SpectralNet: Spectral Clustering using Deep Neural Networks

Defending against Adversarial Images using Basis Functions Transformations

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