
Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language
Current NLP datasets targeting ambiguity can be solved by a native speak...
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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...
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Neural Machine Translation without Embeddings
Many NLP models follow the embedcontextualizepredict paradigm, in whic...
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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...
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Learning to Ask Medical Questions using Reinforcement Learning
We propose a novel reinforcement learningbased approach for adaptive an...
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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...
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Defending against Adversarial Images using Basis Functions Transformations
We study the effectiveness of various approaches that defend against adv...
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SpectralNet: Spectral Clustering using Deep Neural Networks
Spectral clustering is a leading and popular technique in unsupervised d...
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Stochastic Neighbor Embedding separates wellseparated clusters
Stochastic Neighbor Embedding and its variants are widely used dimension...
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Removal of Batch Effects using DistributionMatching Residual Networks
Sources of variability in experimentally derived data include measuremen...
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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 ...
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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...
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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...
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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...
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Provable approximation properties for deep neural networks
We discuss approximation of functions using deep neural nets. Given a fu...
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Diffusion Nets
Nonlinear manifold learning enables highdimensional data analysis, but...
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Uri Shaham
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