Adriana Romero Soriano

Puneet K. Dokania

Mark Coates

Philip Torr

Ser-Nam Lim

Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial fo… (voir plus)r Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings. However, Graph Transformers that use message-passing inherit known issues of message-passing, and differ significantly from Transformers used in other domains, thus making transfer of research advances more difficult. On the other hand, Graph Transformers without message-passing often perform poorly on smaller datasets, where inductive biases are more crucial. To bridge this gap, we propose the Graph Inductive bias Transformer (GRIT) — a new Graph Transformer that incorporates graph inductive biases without using message passing. GRIT is based on several architectural changes that are each theoretically and empirically justified, including: learned relative positional encodings initialized with random walk probabilities, a flexible attention mechanism that updates node and node-pair representations, and injection of degree information in each layer. We prove that GRIT is expressive — it can express shortest path distances and various graph propagation matrices. GRIT achieves state-of-the-art empirical performance across a variety of graph datasets, thus showing the power that Graph Transformers without message-passing can deliver.

2023-01-01

ICML (publié)

doi.org

openreview.net

Revisiting Hotels-50K and Hotel-ID

Aarash Feizi

Arantxa Casanova

Reihaneh Rabbany

In this paper, we propose revisited versions for two recent hotel recognition datasets: Hotels-50K and Hotel-ID. The revisited versions prov… (voir plus)ide evaluation setups with different levels of difﬁculty to better align with the intended real-world application, i.e. countering human trafﬁcking. Real-world scenarios involve hotels and locations that are not captured in the current data sets, therefore it is important to consider evaluation settings where classes are truly unseen. We test this setup using multiple state-of-the-art image retrieval models and show that as expected, the models’ performances decrease as the evaluation gets closer to the real-world unseen settings. The rankings of the best performing models also change across the different evaluation settings, which further motivates using the proposed revisited datasets.

2022-07-20

ArXiv (prépublication)

doi.org

Graph Attention Networks with Positional Embeddings

Liheng Ma

Reihaneh Rabbany

2021-05-09

Advances in Knowledge Discovery and Data Mining (publié)

doi.org

Diet Networks: Thin Parameters for Fat Genomics

pierre luc carrier

Akram Erraqabi

Tristan Sylvain

Alex Auvolat

Etienne Dejoie

Marc-André Legault

Marie-Pierre Dubé

Julie Hussin

Learning tasks such as those involving genomic data often poses a serious challenge: the number of input features can be orders of magnitude… (voir plus) larger than the number of training examples, making it difficult to avoid overfitting, even when using the known regularization techniques. We focus here on tasks in which the input is a description of the genetic variation specific to a patient, the single nucleotide polymorphisms (SNPs), yielding millions of ternary inputs. Improving the ability of deep learning to handle such datasets could have an important impact in medical research, more specifically in precision medicine, where high-dimensional data regarding a particular patient is used to make predictions of interest. Even though the amount of data for such tasks is increasing, this mismatch between the number of examples and the number of inputs remains a concern. Naive implementations of classifier neural networks involve a huge number of free parameters in their first layer (number of input features times number of hidden units): each input feature is associated with as many parameters as there are hidden units. We propose a novel neural network parametrization which considerably reduces the number of free parameters. It is based on the idea that we can first learn or provide a distributed representation for each input feature (e.g. for each position in the genome where variations are observed in data), and then learn (with another neural network called the parameter prediction network) how to map a feature's distributed representation (based on the feature's identity not its value) to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units). This approach views the problem of producing the parameters associated with each feature as a multi-task learning problem. We show experimentally on a population stratification task of interest to medical studies that the proposed approach can significantly reduce both the number of parameters and the error rate of the classifier.

2017-01-01

ICLR.cc/2017/conference (poster)

openreview.net

Diet Networks: Thin Parameters for Fat Genomics

pierre luc carrier

Akram Erraqabi

Tristan Sylvain

Alex Auvolat

Etienne Dejoie

Marc-André Legault

Marie-Pierre Dubé

Julie Hussin

Learning tasks such as those involving genomic data often poses a serious challenge: the number of input features can be orders of magnitude… (voir plus) larger than the number of training examples, making it difficult to avoid overfitting, even when using the known regularization techniques. We focus here on tasks in which the input is a description of the genetic variation specific to a patient, the single nucleotide polymorphisms (SNPs), yielding millions of ternary inputs. Improving the ability of deep learning to handle such datasets could have an important impact in medical research, more specifically in precision medicine, where high-dimensional data regarding a particular patient is used to make predictions of interest. Even though the amount of data for such tasks is increasing, this mismatch between the number of examples and the number of inputs remains a concern. Naive implementations of classifier neural networks involve a huge number of free parameters in their first layer (number of input features times number of hidden units): each input feature is associated with as many parameters as there are hidden units. We propose a novel neural network parametrization which considerably reduces the number of free parameters. It is based on the idea that we can first learn or provide a distributed representation for each input feature (e.g. for each position in the genome where variations are observed in data), and then learn (with another neural network called the parameter prediction network) how to map a feature's distributed representation (based on the feature's identity not its value) to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units). This approach views the problem of producing the parameters associated with each feature as a multi-task learning problem. We show experimentally on a population stratification task of interest to medical studies that the proposed approach can significantly reduce both the number of parameters and the error rate of the classifier.

2017-01-01

ICLR.cc/2017/conference (poster)

openreview.net

Diet Networks: Thin Parameters for Fat Genomic

pierre luc carrier

Akram Erraqabi

Tristan Sylvain

Alex Auvolat

Etienne Dejoie

Marc-andr'e Legault

M. Dubé

Julie Hussin

Learning tasks such as those involving genomic data often poses a serious challenge: the number of input features can be orders of magnitude… (voir plus) larger than the number of training examples, making it difficult to avoid overfitting, even when using the known regularization techniques. We focus here on tasks in which the input is a description of the genetic variation specific to a patient, the single nucleotide polymorphisms (SNPs), yielding millions of ternary inputs. Improving the ability of deep learning to handle such datasets could have an important impact in precision medicine, where high-dimensional data regarding a particular patient is used to make predictions of interest. Even though the amount of data for such tasks is increasing, this mismatch between the number of examples and the number of inputs remains a concern. Naive implementations of classifier neural networks involve a huge number of free parameters in their first layer: each input feature is associated with as many parameters as there are hidden units. We propose a novel neural network parametrization which considerably reduces the number of free parameters. It is based on the idea that we can first learn or provide a distributed representation for each input feature (e.g. for each position in the genome where variations are observed), and then learn (with another neural network called the parameter prediction network) how to map a feature's distributed representation to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units). We show experimentally on a population stratification task of interest to medical studies that the proposed approach can significantly reduce both the number of parameters and the error rate of the classifier.

2016-11-04

ArXiv (prépublication)

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

Arnaud Bergeron

J. Bergstra

Valentin Bisson

Josh Bleecher Snyder

Nicolas Bouchard

Nicolas Boulanger-Lewandowski

Xavier Bouthillier

Alexandre De Brébisson

Olivier Breuleux … (voir 92 de plus)

pierre luc carrier

Kyunghyun Cho

Jan Chorowski

Paul F. Christiano

Tim Cooijmans

Marc-Alexandre Côté

Myriam Côté

Aaron Courville

Yann Dauphin

Olivier Delalleau

Julien Demouth

Guillaume Desjardins

Sander Dieleman

Laurent Dinh

M'elanie Ducoffe

Vincent Dumoulin

Samira Ebrahimi Kahou

Dumitru Erhan

Ziye Fan

Orhan Firat

Mathieu Germain

Xavier Glorot

Ian G Goodfellow

Matthew Graham

Caglar Gulcehre

Philippe Hamel

Iban Harlouchet

Jean-philippe Heng

Balázs Hidasi

Sina Honari

Arjun Jain

S'ebastien Jean

Kai Jia

Mikhail V. Korobov

Vivek Kulkarni

Alex Lamb

Pascal Lamblin

Eric 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

Pierre-Antoine Manzagol

Olivier Mastropietro

R. McGibbon

Roland Memisevic

Bart van Merriënboer

Vincent Michalski

Mehdi Mirza

Alberto Orlandi

Chris Pal

Razvan Pascanu

Mohammad Pezeshki

Colin Raffel

Daniel Renshaw

Matthew David Rocklin

Markus Dr. Roth

Peter Sadowski

John Salvatier

Francois Savard

Jan Schlüter

John D. Schulman

Gabriel Schwartz

Iulian V. Serban

Dmitriy Serdyuk

Samira Shabanian

Etienne Simon

Sigurd Spieckermann

S. Subramanyam

Jakub Sygnowski

Jérémie Tanguay

Gijs van Tulder

Joseph P. Turian

Sebastian Urban

Pascal Vincent

Francesco Visin

Harm de Vries

David Warde-Farley

Dustin J. Webb

M. Willson

Kelvin Xu

Lijun Xue

Li Yao

Saizheng Zhang

Ying Zhang

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 (prépublication)

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

Arnaud Bergeron

J. Bergstra

Valentin Bisson

Josh Bleecher Snyder

Nicolas Bouchard

Nicolas Boulanger-Lewandowski

Xavier Bouthillier

Alexandre De Brébisson

Olivier Breuleux … (voir 92 de plus)

pierre luc carrier

Kyunghyun Cho

Jan Chorowski

Paul F. Christiano

Tim Cooijmans

Marc-Alexandre Côté

Myriam Côté

Aaron Courville

Yann Dauphin

Olivier Delalleau

Julien Demouth

Guillaume Desjardins

Sander Dieleman

Laurent Dinh

M'elanie Ducoffe

Vincent Dumoulin

Samira Ebrahimi Kahou

Dumitru Erhan

Ziye Fan

Orhan Firat

Mathieu Germain

Xavier Glorot

Ian J. Goodfellow

Matthew Graham

Caglar Gulcehre

Philippe Hamel

Iban Harlouchet

Jean-philippe Heng

Balázs Hidasi

Sina Honari

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

Pierre-Antoine Manzagol

Olivier Mastropietro

R. McGibbon

Roland Memisevic

Bart van Merriënboer

Vincent Michalski

Mehdi Mirza

Alberto Orlandi

Chris Pal

Razvan Pascanu

Mohammad Pezeshki

Colin Raffel

Daniel Renshaw

Matthew David Rocklin

Markus Dr. Roth

Peter Sadowski

John Salvatier

Francois Savard

Jan Schlüter

John D. Schulman

Gabriel Schwartz

Iulian V. Serban

Dmitriy Serdyuk

Samira Shabanian

Etienne Simon

Sigurd Spieckermann

S. Subramanyam

Jakub Sygnowski

Jérémie Tanguay

Gijs van Tulder

Joseph P. Turian

Sebastian Urban

Pascal Vincent

Francesco Visin

Harm de Vries

David Warde-Farley

Dustin J. Webb

M. Willson

Kelvin Xu

Lijun Xue

Li Yao

Saizheng Zhang

Ying Zhang

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)