Publications

Dissecting the phenotypic heterogeneity in sensory features in autism spectrum disorder: a factor mixture modelling approach
Julian Tillmann
M. Uljarevic
Daisy Crawley
G. Dumas
Eva Loth
D. Murphy
J. Buitelaar
Tony Charman
Jumana Sara Bonnie Sarah Christian Thomas Carsten Michael Daniel Claudia Yvette Bhismadev Ineke Flavio Dell’ Guillaume Christine Jessica Vincent Pilar David Hannah Joerg Mark H. Emily J. H. Prantik Meng-Chuan Xavier Liogier Michael David J. René Luke Andreas Carolin Nico Laurence Marianne Bob Gahan Antonio M. Barbara Amber Jessica Roberto Roberto Heike Jack Steve C. R. Caroline Marcel P. Ahmad
Jumana Sara Bonnie Sarah Christian Thomas Carsten Michael Ahmad Ambrosino Auyeung Baumeister Beckmann Bourge
Jumana Ahmad
Sara Ambrosino
Bonnie Auyeung
Sarah Baumeister
Christian Beckmann
Thomas Bourgeron
Carsten Bours
Michael Brammer
Daniel Brandeis
Claudia Brogna … (see 39 more)
Yvette de Bruijn
Bhismadev Chakrabarti
Ineke Cornelissen
Flavio Dell’ Acqua
Guillaume Dumas
Christine Ecker
Jessica Faulkner
Vincent Frouin
Pilar Garcés
David Goyard
Hannah Hayward
Joerg F. Hipp
Mark Johnson
Emily J. H. Jones
Prantik Kundu
Meng-Chuan Lai
Xavier Liogier D’ardhuy
Michael V. Lombardo
David J. Lythgoe
René Mandl
Luke Mason
Andreas Meyer-Lindenberg
Carolin Moessnang
Nico Mueller
Laurence O’Dwyer
Marianne Oldehinkel
Bob Oranje
Gahan Pandina
Antonio Persico
Barbara Ruggeri
Amber N. V. Ruigrok
Jessica Sabet
Roberto Sacco
Roberto Toro
Heike Tost
Jack Waldman
Steve C. R. Williams
Caroline Wooldridge
Marcel P. Zwiers
2020-03-05
Molecular Autism (published)
doi.org
RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning
Stefano Alletto
Shenyang Huang
Vincent Francois-Lavet
Yohei Nakata
Guillaume Rabusseau
Almost all neural architecture search methods are evaluated in terms of performance (i.e. test accuracy) of the model structures that it fin… (see more)ds. Should it be the only metric for a good autoML approach? To examine aspects beyond performance, we propose a set of criteria aimed at evaluating the core of autoML problem: the amount of human intervention required to deploy these methods into real world scenarios. Based on our proposed evaluation checklist, we study the effectiveness of a random search strategy for fully automated multimodal neural architecture search. Compared to traditional methods that rely on manually crafted feature extractors, our method selects each modality from a large search space with minimal human supervision. We show that our proposed random search strategy performs close to the state of the art on the AV-MNIST dataset while meeting the desirable characteristics for a fully automated design process.
2020-03-02
ArXiv (preprint)
arxiv.org
arxiv.org
Tensor Networks for Language Modeling
Jacob Miller
Guillaume Rabusseau
John Anthony Terilla
The tensor network formalism has enjoyed over two decades of success in modeling the behavior of complex quantum-mechanical systems, but has… (see more) only recently and sporadically been leveraged in machine learning. Here we introduce a uniform matrix product state (u-MPS) model for probabilistic modeling of sequence data. We identify several distinctive features of this recurrent generative model, notably the ability to condition or marginalize sampling on characters at arbitrary locations within a sequence, with no need for approximate sampling methods. Despite the sequential architecture of u-MPS, we show that a recursive evaluation algorithm can be used to parallelize its inference and training, with a string of length n only requiring parallel time
2020-03-02
ArXiv (preprint)
arxiv.org
arxiv.org
Tensor Networks for Probabilistic Sequence Modeling
Jacob Miller
Guillaume Rabusseau
John Anthony Terilla
Tensor networks are a powerful modeling framework developed for computational many-body physics, which have only recently been applied withi… (see more)n machine learning. In this work we utilize a uniform matrix product state (u-MPS) model for probabilistic modeling of sequence data. We first show that u-MPS enable sequence-level parallelism, with length-n sequences able to be evaluated in depth O(log n). We then introduce a novel generative algorithm giving trained u-MPS the ability to efficiently sample from a wide variety of conditional distributions, each one defined by a regular expression. Special cases of this algorithm correspond to autoregressive and fill-in-the-blank sampling, but more complex regular expressions permit the generation of richly structured text in a manner that has no direct analogue in current generative models. Experiments on synthetic text data find u-MPS outperforming LSTM baselines in several sampling tasks, and demonstrate strong generalization in the presence of limited data.
2020-03-02
International Conference on Artificial Intelligence and Statistics (published)
proceedings.mlr.press
Tensor Networks for Probabilistic Sequence Modeling
Jacob Miller
Guillaume Rabusseau
John Anthony Terilla
Tensor networks are a powerful modeling framework developed for computational many-body physics, which have only recently been applied withi… (see more)n machine learning. In this work we utilize a uniform matrix product state (u-MPS) model for probabilistic modeling of sequence data. We first show that u-MPS enable sequence-level parallelism, with length-n sequences able to be evaluated in depth O(log n). We then introduce a novel generative algorithm giving trained u-MPS the ability to efficiently sample from a wide variety of conditional distributions, each one defined by a regular expression. Special cases of this algorithm correspond to autoregressive and fill-in-the-blank sampling, but more complex regular expressions permit the generation of richly structured text in a manner that has no direct analogue in current generative models. Experiments on synthetic text data find u-MPS outperforming LSTM baselines in several sampling tasks, and demonstrate strong generalization in the presence of limited data.
2020-03-02
International Conference on Artificial Intelligence and Statistics (published)
dblp.uni-trier.de
Seven pillars of precision digital health and medicine
Arash Shaban-Nejad
Martin Michalowski
Niels Peek
John S. Brownstein
David Buckeridge
2020-03-01
Artificial Intelligence in Medicine (published)
doi.org
Machine learning analysis of exome trios to contrast the genomic architecture of autism and schizophrenia
Sameer Sardaar
Bill Qi
Alexandre Dionne-Laporte
Guy. A. Rouleau
Reihaneh Rabbany
Yannis Trakadis
2020-02-28
BMC Psychiatry (published)
doi.org
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence
Nicolas Loizou
Sharan Vaswani
Issam Hadj Laradji
Simon Lacoste-Julien
We propose a stochastic variant of the classical Polyak step-size (Polyak, 1987) commonly used in the subgradient method. Although computing… (see more) the Polyak step-size requires knowledge of the optimal function values, this information is readily available for typical modern machine learning applications. Consequently, the proposed stochastic Polyak step-size (SPS) is an attractive choice for setting the learning rate for stochastic gradient descent (SGD). We provide theoretical convergence guarantees for SGD equipped with SPS in different settings, including strongly convex, convex and non-convex functions. Furthermore, our analysis results in novel convergence guarantees for SGD with a constant step-size. We show that SPS is particularly effective when training over-parameterized models capable of interpolating the training data. In this setting, we prove that SPS enables SGD to converge to the true solution at a fast rate without requiring the knowledge of any problem-dependent constants or additional computational overhead. We experimentally validate our theoretical results via extensive experiments on synthetic and real datasets. We demonstrate the strong performance of SGD with SPS compared to state-of-the-art optimization methods when training over-parameterized models.
2020-02-24
ArXiv (preprint)
arxiv.org
arxiv.org
The Geometry of Sign Gradient Descent
Lukas Balles
Fabian Pedregosa
Nicolas Le Roux
2020-02-19
ArXiv (preprint)
openreview.net
openreview.net
Minimax Theorem for Latent Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets
Gauthier Gidel
D. Balduzzi
Wojciech M. Czarnecki
M. Garnelo
Yoram Bachrach
Adversarial training, a special case of multi-objective optimization, is an increasingly useful tool in machine learning. For example, two-p… (see more)layer zero-sum games are important for generative modeling (GANs) and for mastering games like Go or Poker via self-play. A classic result in Game Theory states that one must mix strategies, as pure equilibria may not exist. Surprisingly, machine learning practitioners typically train a \emph{single} pair of agents -- instead of a pair of mixtures -- going against Nash's principle. Our main contribution is a notion of limited-capacity-equilibrium for which, as capacity grows, optimal agents -- not mixtures -- can learn increasingly expressive and realistic behaviors. We define \emph{latent games}, a new class of game where agents are mappings that transform latent distributions. Examples include generators in GANs, which transform Gaussian noise into distributions on images, and StarCraft II agents, which transform sampled build orders into policies. We show that minimax equilibria in latent games can be approximated by a \emph{single} pair of dense neural networks. Finally, we apply our latent game approach to solve differentiable Blotto, a game with an infinite strategy space.
2020-02-14
ArXiv (preprint)
arxiv.org
arxiv.org
Minimax Theorem for Latent Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets
Gauthier Gidel
D. Balduzzi
Wojciech M. Czarnecki
M. Garnelo
Yoram Bachrach
Adversarial training, a special case of multi-objective optimization, is an increasingly useful tool in machine learning. For example, two-p… (see more)layer zero-sum games are important for generative modeling (GANs) and for mastering games like Go or Poker via self-play. A classic result in Game Theory states that one must mix strategies, as pure equilibria may not exist. Surprisingly, machine learning practitioners typically train a \emph{single} pair of agents -- instead of a pair of mixtures -- going against Nash's principle. Our main contribution is a notion of limited-capacity-equilibrium for which, as capacity grows, optimal agents -- not mixtures -- can learn increasingly expressive and realistic behaviors. We define \emph{latent games}, a new class of game where agents are mappings that transform latent distributions. Examples include generators in GANs, which transform Gaussian noise into distributions on images, and StarCraft II agents, which transform sampled build orders into policies. We show that minimax equilibria in latent games can be approximated by a \emph{single} pair of dense neural networks. Finally, we apply our latent game approach to solve differentiable Blotto, a game with an infinite strategy space.
2020-02-14
ArXiv (preprint)
arxiv.org
arxiv.org
Saliency Enhancement using Gradient Domain Edges Merging
Dominique Beaini
Sofiane Wozniak Achiche
Alexandre Duperre
Maxime Raison
In recent years, there has been a rapid progress in solving the binary problems in computer vision, such as edge detection which finds the b… (see more)oundaries of an image and salient object detection which finds the important object in an image. This progress happened thanks to the rise of deep-learning and convolutional neural networks (CNN) which allow to extract complex and abstract features. However, edge detection and saliency are still two different fields and do not interact together, although it is intuitive for a human to detect salient objects based on its boundaries. Those features are not well merged in a CNN because edges and surfaces do not intersect since one feature represents a region while the other represents boundaries between different regions. In the current work, the main objective is to develop a method to merge the edges with the saliency maps to improve the performance of the saliency. Hence, we developed the gradient-domain merging (GDM) which can be used to quickly combine the image-domain information of salient object detection with the gradient-domain information of the edge detection. This leads to our proposed saliency enhancement using edges (SEE) with an average improvement of the F-measure of at least 3.4 times higher on the DUT-OMRON dataset and 6.6 times higher on the ECSSD dataset, when compared to competing algorithm such as denseCRF and BGOF. The SEE algorithm is split into 2 parts, SEE-Pre for preprocessing and SEE-Post pour postprocessing.
2020-02-11
ArXiv (preprint)
arxiv.org
arxiv.org