Bart Van Merriënboer

Amanda Navine

Patrick Hart

Ben Williams

Timothy A. C. Lamont

Tries B. Rasak

Mars Coral Restoration Team

Sheryn Brodie

Brendan Doohan

Philip Eichinski

Paul Roe

Lin Schwarzkopf

Tom Denton

2025-05-05

ArXiv (prépublication)

The Search for Squawk: Agile Modeling in Bioacoustics

Vincent Dumoulin

Otilia Stretcu

Jenny Hamer

Lauren Harrell

Rob Laber

Hugo Larochelle

Amanda Navine

Patrick Hart

Ben Williams

Timothy A. C. Lamont

Tries B. Rasak

Mars Coral Restoration Team

Sheryn Brodie

Brendan Doohan

Philip Eichinski

Paul Roe

Lin Schwarzkopf

Tom Denton

2025-05-05

ArXiv (prépublication)

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

Courtney Paquette

Fabian Pedregosa

2022-02-15

Foundations of Computational Mathematics (publié)

doi.org

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

Courtney Paquette

Fabian Pedregosa

2020-06-08

ArXiv (preprint)

doi.org

On the interplay between noise and curvature and its effect on optimization and generalization

Valentin Thomas

Fabian Pedregosa

The speed at which one can minimize an expected loss using stochastic methods depends on two properties: the curvature of the loss and the v… (voir plus)ariance of the gradients. While most previous works focus on one or the other of these properties, we explore how their interaction affects optimization speed. Further, as the ultimate goal is good generalization performance, we clarify how both curvature and noise are relevant to properly estimate the generalization gap. Realizing that the limitations of some existing works stems from a confusion between these matrices, we also clarify the distinction between the Fisher matrix, the Hessian, and the covariance matrix of the gradients.

2020-01-01

AISTATS (publié)

proceedings.mlr.press

On the interplay between noise and curvature and its effect on optimization and generalization

Valentin Thomas

Fabian Pedregosa

Pierre-Antoine Mangazol

The speed at which one can minimize an expected loss using stochastic methods depends on two properties: the curvature of the loss and the v… (voir plus)ariance of the gradients. While most previous works focus on one or the other of these properties, we explore how their interaction affects optimization speed. Further, as the ultimate goal is good generalization performance, we clarify how both curvature and noise are relevant to properly estimate the generalization gap. Realizing that the limitations of some existing works stems from a confusion between these matrices, we also clarify the distinction between the Fisher matrix, the Hessian, and the covariance matrix of the gradients.

2019-06-19

ArXiv (prépublication)

Information matrices and generalization

Valentin Thomas

Fabian Pedregosa

This work revisits the use of information criteria to characterize the generalization of deep learning models. In particular, we empirically… (voir plus) demonstrate the effectiveness of the Takeuchi information criterion (TIC), an extension of the Akaike information criterion (AIC) for misspecified models, in estimating the generalization gap, shedding light on why quantities such as the number of parameters cannot quantify generalization. The TIC depends on both the Hessian of the loss H and the covariance of the gradients C. By exploring the similarities and differences between these two matrices as well as the Fisher information matrix F, we study the interplay between noise and curvature in deep models. We also address the question of whether C is a reasonable approximation to F, as is commonly assumed.

2019-06-18

arXiv.org (preprint)

dblp.uni-trier.de

Information matrices and generalization

Valentin Thomas

Fabian Pedregosa

This work revisits the use of information criteria to characterize the generalization of deep learning models. In particular, we empirically… (voir plus) demonstrate the effectiveness of the Takeuchi information criterion (TIC), an extension of the Akaike information criterion (AIC) for misspecified models, in estimating the generalization gap, shedding light on why quantities such as the number of parameters cannot quantify generalization. The TIC depends on both the Hessian of the loss H and the covariance of the gradients C. By exploring the similarities and differences between these two matrices as well as the Fisher information matrix F, we study the interplay between noise and curvature in deep models. We also address the question of whether C is a reasonable approximation to F, as is commonly assumed.

2019-06-18

arXiv.org (prépublication)

dblp.uni-trier.de

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

Olivier Breuleux

Arnaud Bergeron

Pascal Lamblin

We review the current state of automatic differentiation (AD) for array programming in machine learning (ML), including the different approa… (voir plus)ches such as operator overloading (OO) and source transformation (ST) used for AD, graph-based intermediate representations for programs, and source languages. Based on these insights, we introduce a new graph-based intermediate representation (IR) which specifically aims to efficiently support fully-general AD for array programming. Unlike existing dataflow programming representations in ML frameworks, our IR naturally supports function calls, higher-order functions and recursion, making ML models easier to implement. The ability to represent closures allows us to perform AD using ST without a tape, making the resulting derivative (adjoint) program amenable to ahead-of-time optimization using tools from functional language compilers, and enabling higher-order derivatives. Lastly, we introduce a proof of concept compiler toolchain called Myia which uses a subset of Python as a front end.

2018-10-01

ArXiv (prépublication)

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

Olivier Breuleux

Arnaud Bergeron

Pascal Lamblin

We review the current state of automatic differentiation (AD) for array programming in machine learning (ML), including the different approa… (voir plus)ches such as operator overloading (OO) and source transformation (ST) used for AD, graph-based intermediate representations for programs, and source languages. Based on these insights, we introduce a new graph-based intermediate representation (IR) which specifically aims to efficiently support fully-general AD for array programming. Unlike existing dataflow programming representations in ML frameworks, our IR naturally supports function calls, higher-order functions and recursion, making ML models easier to implement. The ability to represent closures allows us to perform AD using ST without a tape, making the resulting derivative (adjoint) program amenable to ahead-of-time optimization using tools from functional language compilers, and enabling higher-order derivatives. Lastly, we introduce a proof of concept compiler toolchain called Myia which uses a subset of Python as a front end.

2018-01-01

NeurIPS (publié)

Automatic Differentiation in Myia

Olivier Breuleux

Nicolas Boulanger-Lewandowski

Automatic differentiation is an essential feature of machine learning frameworks. However, its implementation in existing frameworks often h… (voir plus)as limitations. In dataflow programming frameworks such as Theano or TensorFlow the representation used makes supporting higher-order gradients difficult. On the other hand, operator overloading frameworks such as PyTorch are flexible, but do not lend themselves well to optimization. With Myia, we attempt to have the best of both worlds: Building on the work by Pearlmutter and Siskind we implement a first-order gradient operator for a subset of the Python programming language.

2017-10-28

(publié)

www.semanticscholar.org

Theano: A Python framework for fast computation of mathematical expressions

Rami Al-rfou'

Guillaume Alain

Amjad Almahairi

Christof Angermüller

Dzmitry Bahdanau

Nicolas Ballas

Frédéric Bastien

Justin S. Bayer

A. Belikov

A. Belopolsky

J. Bergstra

Josh Bleecher Snyder

Xavier Bouthillier

Alexandre De Brébisson

Olivier Breuleux … (voir 92 de plus)

Paul F. Christiano

Marc-Alexandre Côté

Myriam Côté

Julien Demouth

Sander Dieleman

M'elanie Ducoffe

Samira Ebrahimi Kahou

Dumitru Erhan

Ziye Fan

Orhan Firat

Mathieu Germain

Xavier Glorot

Ian J. Goodfellow

Matthew Graham

Balázs Hidasi

Arjun Jain

S'ebastien Jean

Kai Jia

Mikhail V. Korobov

Vivek Kulkarni

Alex Lamb

Pascal Lamblin

Eric P. Larsen

César Laurent

S. Lee

Simon-mark Lefrancois

Simon Lemieux

Nicholas Léonard

Zhouhan Lin

J. Livezey

Cory R. Lorenz

Jeremiah L. Lowin

Qianli M. Ma

R. McGibbon

Mehdi Mirza

Alberto Orlandi

Chris Pal

Razvan Pascanu

Mohammad Pezeshki

Colin Raffel

Daniel Renshaw

Matthew David Rocklin

Adriana Romero Soriano

Markus Dr. Roth

Peter Sadowski

John Salvatier

François Savard

Jan Schlüter

John D. Schulman

Gabriel Schwartz

Iulian V. Serban

Dmitriy Serdyuk

Samira Shabanian

Etienne Simon

Sigurd Spieckermann

S. Subramanyam

Jakub Sygnowski

Jeremie Tanguay

Gijs van Tulder

Joseph P. Turian

Sebastian Urban

Dustin J. Webb

M. Willson

Lijun Xue

Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficie… (voir plus)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.

2016-05-09

ArXiv (preprint)