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09/15/2022
TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations
We present TwHIN-BERT, a multilingual language model trained on in-domai...
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06/10/2022
The Generalized Eigenvalue Problem as a Nash Equilibrium
The generalized eigenvalue problem (GEP) is a fundamental concept in num...
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01/13/2022
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?
Despite recent progress made by self-supervised methods in representatio...
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02/08/2021
EigenGame Unloaded: When playing games is better than optimizing
We build on the recently proposed EigenGame that views eigendecompositio...
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10/15/2020
Representation Learning via Invariant Causal Mechanisms
Self-supervised learning has emerged as a strategy to reduce the relianc...
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10/01/2020
EigenGame: PCA as a Nash Equilibrium
We present a novel view on principal component analysis (PCA) as a compe...
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02/06/2020
Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning
Recent research on reinforcement learning in pure-conflict and pure-comm...
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01/15/2019
Spectrogram Feature Losses for Music Source Separation
In this paper we study deep learning-based music source separation, and ...
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08/11/2018
Neural Importance Sampling
We propose to use deep neural networks for generating samples in Monte C...
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04/09/2018
A Fully Progressive Approach to Single-Image Super-Resolution
Recent deep learning approaches to single image super-resolution have ac...
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04/03/2018
PhaseNet for Video Frame Interpolation
Most approaches for video frame interpolation require accurate dense cor...
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09/15/2017
Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks
We present a technique for efficiently synthesizing images of atmospheri...
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03/01/2017
Preserving Differential Privacy Between Features in Distributed Estimation
Privacy is crucial in many applications of machine learning. Legal, ethi...
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02/28/2017
The Shattered Gradients Problem: If resnets are the answer, then what is the question?
A long-standing obstacle to progress in deep learning is the problem of ...
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11/21/2016
Scalable Adaptive Stochastic Optimization Using Random Projections
Adaptive stochastic gradient methods such as AdaGrad have gained popular...
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11/07/2016
Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks
Modern convolutional networks, incorporating rectifiers and max-pooling,...
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06/11/2015
Variance Reduced Stochastic Gradient Descent with Neighbors
Stochastic Gradient Descent (SGD) is a workhorse in machine learning, ye...
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06/08/2015
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections
We present DUAL-LOCO, a communication-efficient algorithm for distribute...
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06/24/2013
Correlated random features for fast semi-supervised learning
This paper presents Correlated Nystrom Views (XNV), a fast semi-supervis...
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03/05/2012
Subspace clustering of high-dimensional data: a predictive approach
In several application domains, high-dimensional observations are collec...
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02/02/2012
