Publications

Patterns of autism symptoms: hidden structure in the ADOS and ADI-R instruments

Jeremy Lefort-Besnard

Kai Vogeley

Leonhard Schilbach

Gael Varoquaux

Bertrand Thirion

Guillaume Dumas

Danilo Bzdok

2019-09-26

Translational Psychiatry (publié)

doi.org

Assessing Generalization in TD methods for Deep Reinforcement Learning

Emmanuel Bengio

Doina Precup

Joelle Pineau

2019-09-24

(publié)

openreview.net

{COMPANYNAME}11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery

Guillaume Androz

Pierre Fecteau

2019-09-24

(publié)

openreview.net

Equilibrium Propagation with Continual Weight Updates

Maxence Ernoult

Julie Grollier

Damien Querlioz

Yoshua Bengio

Benjamin Scellier

Equilibrium Propagation (EP) is a learning algorithm that bridges Machine Learning and Neuroscience, by computing gradients closely matching… (voir plus) those of Backpropagation Through Time (BPTT), but with a learning rule local in space. Given an input

2019-09-24

ArXiv (prépublication)

openreview.net

Equivariant Entity-Relationship Networks

Devon Graham

Siamak Ravanbakhsh

The relational model is a ubiquitous representation of big-data, in part due to its extensive use in databases. In this paper, we propose th… (voir plus)e Equivariant Entity-Relationship Network (EERN), which is a Multilayer Perceptron equivariant to the symmetry transformations of the Entity-Relationship model. To this end, we identify the most expressive family of linear maps that are exactly equivariant to entity relationship symmetries, and further show that they subsume recently introduced equivariant maps for sets, exchangeable tensors, and graphs. The proposed feed-forward layer has linear complexity in the data and can be used for both inductive and transductive reasoning about relational databases, including database embedding, and the prediction of missing records. This provides a principled theoretical foundation for the application of deep learning to one of the most abundant forms of data. Empirically, EERN outperforms different variants of coupled matrix tensor factorization in both synthetic and real-data experiments.

2019-09-24

(publié)

openreview.net

HighRes-net: Multi-Frame Super-Resolution by Recursive Fusion

Michel Deudon

Alfredo Kalaitzis

Md Rifat Arefin

Israel Goytom

Zhichao Lin

Kris Sankaran

Vincent Michalski

S Ebrahimi Kahou

Julien Cornebise

Yoshua Bengio

2019-09-24

(publié)

openreview.net

Learning Neural Causal Models from Unknown Interventions

Nan Rosemary Ke

Christopher Pal

Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from obs… (voir plus)ervational data. However, there are theoretical limitations on the identifiability of underlying structures obtained from observational data alone. Interventional data provides much richer information about the underlying data-generating process. However, the extension and application of methods designed for observational data to include interventions is not straightforward and remains an open problem. In this paper we provide a general framework based on continuous optimization and neural networks to create models for the combination of observational and interventional data. The proposed method is even applicable in the challenging and realistic case that the identity of the intervened upon variable is unknown. We examine the proposed method in the setting of graph recovery both de novo and from a partially-known edge set. We establish strong benchmark results on several structure learning tasks, including structure recovery of both synthetic graphs as well as standard graphs from the Bayesian Network Repository.

2019-09-24

ArXiv (prépublication)

openreview.net

Selfish Emergent Communication

Michael Noukhovitch

Travis LaCroix

Aaron Courville

2019-09-24

(publié)

openreview.net

SPECTRA: Sparse Entity-centric Transitions

Rim Assouel

Yoshua Bengio

Learning an agent that interacts with objects is ubiquituous in many RL tasks. In most of them the agent’s actions have sparse effects : o… (voir plus)nly a small subset of objects in the visual scene will be affected by the action taken. We introduce SPECTRA, a model for learning slot-structured transitions from raw visual observations that embodies this sparsity assumption. Our model is composed of a perception module that decomposes the visual scene into a set of latent objects representations (i.e. slot-structured) and a transition module that predicts the next latent set slot-wise and in a sparse way. We show that learning a perception module jointly with a sparse slot-structured transition model not only biases the model towards more entity-centric perceptual groupings but also enables intrinsic exploration strategy that aims at maximizing the number of objects changed in the agents trajectory.

2019-09-24

(publié)

openreview.net