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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...
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12/14/2022
Causes and Cures for Interference in Multilingual Translation
Multilingual machine translation models can benefit from synergy between...
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08/01/2022
Efficient Long-Text Understanding with Short-Text Models
Transformer-based pretrained language models (LMs) are ubiquitous across...
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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...
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01/10/2022
SCROLLS: Standardized CompaRison Over Long Language Sequences
NLP benchmarks have largely focused on short texts, such as sentences an...
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10/12/2021
Single Independent Component Recovery and Applications
Latent variable discovery is a central problem in data analysis with a b...
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10/11/2021
Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features
Modern datasets often contain large subsets of correlated features and n...
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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...
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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...
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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...
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08/21/2020
Neural Machine Translation without Embeddings
Many NLP models follow the embed-contextualize-predict paradigm, in whic...
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07/09/2020
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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03/31/2020
Learning to Ask Medical Questions using Reinforcement Learning
We propose a novel reinforcement learning-based approach for adaptive an...
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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...
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03/28/2018
Defending against Adversarial Images using Basis Functions Transformations
We study the effectiveness of various approaches that defend against adv...
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01/04/2018
SpectralNet: Spectral Clustering using Deep Neural Networks
Spectral clustering is a leading and popular technique in unsupervised d...
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02/09/2017
Stochastic Neighbor Embedding separates well-separated clusters
Stochastic Neighbor Embedding and its variants are widely used dimension...
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10/13/2016
Removal of Batch Effects using Distribution-Matching Residual Networks
Sources of variability in experimentally derived data include measuremen...
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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 ...
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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...
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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...
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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...
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09/24/2015
Provable approximation properties for deep neural networks
We discuss approximation of functions using deep neural nets. Given a fu...
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06/25/2015
