Yoshua Bengio

Scaling in the Service of Reasoning & Model-Based ML

April 4, 2023

Yoshua Bengio

Edward J. Hu

A collaboration between Mila and Relation Therapeutics to discover novel synergistic combinations of drugs in vitro

March 23, 2022

Paul Bertin

Jake P. Taylor-King

Yoshua Bengio

March 15, 2022

Generative Flow Networks

Yoshua Bengio

Publications

Variance Reduction in SGD by Distributed Importance Sampling

Humans are able to accelerate their learning by selecting training materials that are the most informative and at the appropriate level of d… (see more)ifficulty. We propose a framework for distributing deep learning in which one set of workers search for the most informative examples in parallel while a single worker updates the model on examples selected by importance sampling. This leads the model to update using an unbiased estimate of the gradient which also has minimum variance when the sampling proposal is proportional to the L2-norm of the gradient. We show experimentally that this method reduces gradient variance even in a context where the cost of synchronization across machines cannot be ignored, and where the factors for importance sampling are not updated instantly across the training set.

2016-02-16

ICLR.cc/2016/workshop (unknown)

openreview.net

Former NASA chief unveils $ 100 million neural chip maker KnuEdge

C. Strasser

Dean Takahashi

Tim Klinger

Gerald Tesauro

Kartik Talamadupula

Bowen Zhou

Yoshua Bengio

Aaron Courville

Medium, Moore Data, Carly Strasser from June 07, 2016 Open access to research articles has been in the news quite a bit lately (see the SciH… (see more)ub controversy, the preprints in biology discussion, and the European Union’s recent announcement). The Data-Driven Discovery team at the Moore Foundation has also been discussing open access, particularly as it relates to the publications generated by our #MooreData researchers. Our grantee population is fairly progressive when it comes to open science, and many of the outputs that they generate are already publicly available (including proposals, software, workflows, and publications). It is therefore easy for us to imagine that they would embrace a policy that mandates open access for research articles that they produce. That said, we are always open to discussions!

2015-12-31

(published)

www.semanticscholar.org

Professor Forcing: A New Algorithm for Training Recurrent Networks

The Teacher Forcing algorithm trains recurrent networks by supplying observed sequence values as inputs during training and using the networ… (see more)k’s own one-step-ahead predictions to do multi-step sampling. We introduce the Professor Forcing algorithm, which uses adversarial domain adaptation to encourage the dynamics of the recurrent network to be the same when training the network and when sampling from the network over multiple time steps. We apply Professor Forcing to language modeling, vocal synthesis on raw waveforms, handwriting generation, and image generation. Empirically we find that Professor Forcing acts as a regularizer, improving test likelihood on character level Penn Treebank and sequential MNIST. We also find that the model qualitatively improves samples, especially when sampling for a large number of time steps. This is supported by human evaluation of sample quality. Trade-offs between Professor Forcing and Scheduled Sampling are discussed. We produce T-SNEs showing that Professor Forcing successfully makes the dynamics of the network during training and sampling more similar.

2015-12-31

Advances in Neural Information Processing Systems 29 (NIPS 2016) (published)

Nicolas Boulanger-Lewandowski

Theano: A Python framework for fast computation of mathematical expressions

Rami Al-Rfou

Guillaume Alain

Amjad Almahairi

Christof Angermueller

Dzmitry Bahdanau

Nicolas Ballas

Frédéric Bastien

Justin Bayer

Anatoly Belikov

Alexander Belopolsky

Josh Bleecher Snyder

Xavier Bouthillier

Alexandre De Brébisson

Olivier Breuleux … (see 92 more)

Pierre-Luc Carrier

Paul Christiano

Myriam Côté

Yann N. Dauphin

Julien Demouth

Sander Dieleman

Samira Ebrahimi Kahou

Ziye Fan

Mathieu Germain

Matt Graham

Balázs Hidasi

Arjun Jain

Kai Jia

Mikhail Korobov

Vivek Kulkarni

Alex Lamb

Pascal Lamblin

Eric Larsen

César Laurent

Sean Lee

Simon Lefrancois

Simon Lemieux

Nicholas Léonard

Zhouhan Lin

Jesse A. Livezey

Cory Lorenz

Jeremiah Lowin

Qianli Ma

Pierre-Antoine Manzagol

Robert T. McGibbon

Mehdi Mirza

Alberto Orlandi

Christopher Pal

Razvan Pascanu

Mohammad Pezeshki

Colin Raffel

Daniel Renshaw

Matthew Rocklin

Adriana Romero

Markus Roth

Peter Sadowski

John Salvatier

François Savard

Jan Schlüter

John Schulman

Gabriel Schwartz

Iulian Vlad Serban

Dmitriy Serdyuk

Samira Shabanian

Etienne Simon

Sigurd Spieckermann

S. Ramana Subramanyam

Gijs van Tulder

Sebastian Urban

Dustin J. Webb

Matthew Willson

Lijun Xue

Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficie… (see more)ntly. Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements. Theano is being actively and continuously developed since 2008, multiple frameworks have been built on top of it and it has been used to produce many state-of-the-art machine learning models. The present article is structured as follows. Section I provides an overview of the Theano software and its community. Section II presents the principal features of Theano and how to use them, and compares them with other similar projects. Section III focuses on recently-introduced functionalities and improvements. Section IV compares the performance of Theano against Torch7 and TensorFlow on several machine learning models. Section V discusses current limitations of Theano and potential ways of improving it.

2015-12-31

arXiv (preprint)

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations

David Krueger

Nan Rosemary Ke

Christopher Pal

We propose zoneout, a novel method for regularizing RNNs. At each timestep, zoneout stochastically forces some hidden units to maintain thei… (see more)r previous values. Like dropout, zoneout uses random noise to train a pseudo-ensemble, improving generalization. But by preserving instead of dropping hidden units, gradient information and state information are more readily propagated through time, as in feedforward stochastic depth networks. We perform an empirical investigation of various RNN regularizers, and find that zoneout gives significant performance improvements across tasks. We achieve competitive results with relatively simple models in character- and word-level language modelling on the Penn Treebank and Text8 datasets, and combining with recurrent batch normalization yields state-of-the-art results on permuted sequential MNIST.

2015-12-31

arXiv (preprint)

Task Loss Estimation for Sequence Prediction

Nan Rosemary Ke

2015-11-18

ArXiv (preprint)

openreview.net

Describing Multimedia Content Using Attention-Based Encoder-Decoder Networks

Kyunghyun Cho

Aaron Courville

Yoshua Bengio

Whereas deep neural networks were first mostly used for classification tasks, they are rapidly expanding in the realm of structured output p… (see more)roblems, where the observed target is composed of multiple random variables that have a rich joint distribution, given the input. In this paper we focus on the case where the input also has a rich structure and the input and output structures are somehow related. We describe systems that learn to attend to different places in the input, for each element of the output, for a variety of tasks: machine translation, image caption generation, video clip description, and speech recognition. All these systems are based on a shared set of building blocks: gated recurrent neural networks and convolutional neural networks, along with trained attention mechanisms. We report on experimental results with these systems, showing impressively good performance and the advantage of the attention mechanism.

2015-10-31

IEEE Transactions on Multimedia (published)

Generative Adversarial Nets

Moez Krichen

Ian J. Goodfellow

Mehdi Mirza

Generative Adversarial Networks (GANs) are very popular frameworks for generating high-quality data, and are immensely used in both the acad… (see more)emia and industry in many domains. Arguably, their most substantial impact has been in the area of computer vision, where they achieve state-of-the-art image generation. This chapter gives an introduction to GANs, by discussing their principle mechanism and presenting some of their inherent problems during training and evaluation. We focus on these three issues: (1) mode collapse, (2) vanishing gradients, and (3) generation of low-quality images. We then list some architecture-variant and loss-variant GANs that remedy the above challenges. Lastly, we present two utilization examples of GANs for real-world applications: Data augmentation and face images generation.

2014-06-09

Communications of the ACM (published)

Statistical Language and Speech Processing

Yoshua Bengio

Fethi Bougares

Horia Cucu

Corneliu Burileanu

Myung-Jae Kim

Il-ho Yang

Jordan Rodu

Dean Phillips Foster

Weichen Wu

Stefan Bott

Felix Stahlberg

Luis A. Trindade

Hao Wang

2012-12-31

Lecture Notes in Computer Science (published)

Theano: Deep Learning on GPUs with Python

Frédéric Bastien

Pascal Lamblin

Ian G Goodfellow

In this paper, we present Theano 1 , a framework in the Python programming language for defining, optimizing and evaluating expressions invo… (see more)lving high-level operations on tensors. Theano offers most of NumPy’s functionality, but adds automatic symbolic differentiation, GPU support, and faster expression evaluation. Theano is a general mathematical tool, but it was developed with the goal of facilitating research in deep learning. The Deep Learning Tutorials 2 introduce recent advances in deep learning, and showcase how Theano

2011-12-31

(published)

www.semanticscholar.org

Theano: A CPU and GPU Math Compiler in Python

James Bergstra

Olivier Breuleux

Frédéric Bastien

Pascal Lamblin

Theano is a compiler for mathematical expressions in Python that combines the convenience of NumPy's syntax with the speed of optimized nati… (see more)ve machine language. The user composes mathematical expressions in a high-level description that mimics NumPy's syntax and semantics, while being statically typed and functional (as opposed to imperative). These expressions allow Theano to provide symbolic differentiation. Before performing computation, Theano optimizes the choice of expressions, translates them into C++ (or CUDA for GPU), compiles them into dynamically loaded Python modules, all automatically. Common machine learn- ing algorithms implemented with Theano are from 1:6 to 7:5 faster than competitive alternatives (including those implemented with C/C++, NumPy/SciPy and MATLAB) when compiled for the CPU and between 6:5 and 44 faster when compiled for the GPU. This paper illustrates how to use Theano, outlines the scope of the compiler, provides benchmarks on both CPU and GPU processors, and explains its overall design.

2009-12-31

Proceedings of the Python in Science Conference (published)