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Featured Co-authors

Yoshua Bengio
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Alexander M. Rush
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∙ 02/13/2023

In Search for a Generalizable Method for Source Free Domain Adaptation

Source-free domain adaptation (SFDA) is compelling because it allows ada...

0 Malik Boudiaf, et al. ∙

∙ 01/13/2022

GradMax: Growing Neural Networks using Gradient Information

The architecture and the parameters of neural networks are often optimiz...

0 Utku Evci, et al. ∙

∙ 06/08/2020

Halting Time is Predictable for Large Models: A Universality Property and Average-case Analysis

Average-case analysis computes the complexity of an algorithm averaged o...

8 Courtney Paquette, et al. ∙

∙ 06/27/2019

Fast Training of Sparse Graph Neural Networks on Dense Hardware

Graph neural networks have become increasingly popular in recent years d...

4 Matej Balog, et al. ∙

∙ 06/18/2019

Information matrices and generalization

This work revisits the use of information criteria to characterize the g...

4 Valentin Thomas, et al. ∙

∙ 10/26/2018

Automatic differentiation in ML: Where we are and where we should be going

We review the current state of automatic differentiation (AD) for array ...

0 Bart van Merriënboer, et al. ∙

∙ 09/25/2018

Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming

The need to efficiently calculate first- and higher-order derivatives of...

0 Bart van Merriënboer, et al. ∙

∙ 11/07/2017

Tangent: Automatic Differentiation Using Source Code Transformation in Python

Automatic differentiation (AD) is an essential primitive for machine lea...

0 Bart van Merriënboer, et al. ∙

∙ 07/03/2017

Multiscale sequence modeling with a learned dictionary

We propose a generalization of neural network sequence models. Instead o...

0 Bart van Merriënboer, et al. ∙

∙ 05/09/2016

Theano: A Python framework for fast computation of mathematical expressions

Theano is a Python library that allows to define, optimize, and evaluate...

0 The Theano Development Team, et al. ∙

∙ 06/01/2015

Blocks and Fuel: Frameworks for deep learning

We introduce two Python frameworks to train neural networks on large dat...

0 Bart van Merriënboer, et al. ∙

∙ 02/19/2015

Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks

One long-term goal of machine learning research is to produce methods th...

0 Jason Weston, et al. ∙

∙ 09/03/2014

Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation

The authors of (Cho et al., 2014a) have shown that the recently introduc...

0 Jean Pouget-Abadie, et al. ∙

∙ 06/03/2014

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

In this paper, we propose a novel neural network model called RNN Encode...

0 Kyunghyun Cho, et al. ∙

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