Publications

S2RMs: Spatially Structured Recurrent Modules
Nasim Rahaman
Anirudh Goyal
Muhammad Waleed Gondal
Manuel Wüthrich
Stefan Bauer
Y. Sharma
Yoshua Bengio
Bernhard Schölkopf
2020-07-13
ArXiv (prépublication)
arxiv.org
arxiv.org
Towards an Unsupervised Method for Model Selection in Few-Shot Learning
Simon Guiroy
Vikas Verma
Chris Pal
The study of generalization of neural networks in gradient-based meta-learning has recently great research interest. Previous work on the st… (voir plus)udy of the objective landscapes within the scope of few-shot classification empirically demonstrated that generalization to new tasks might be linked to the average inner product between their respective gradients vectors (Guiroy et al., 2019). Following that work, we study the effect that meta-training has on the learned space of representation of the network. Notably, we demonstrate that the global similarity in the space of representation, measured by the average inner product between the embeddings of meta-test examples, also correlates to generalization. Based on these observations, we propose a novel model-selection criterion for gradient-based meta-learning and experimentally validate its effectiveness.
2020-07-13
ICML.cc/2020/Workshop/LifelongML (inconnu)
openreview.net
openreview.net
Data-Efficient Reinforcement Learning with Momentum Predictive Representations
Max Schwarzer
Ankesh Anand
Rishab Goel
(Rex) Devon Hjelm
Aaron Courville
Philip Bachman
While deep reinforcement learning excels at solving tasks where large amounts of data can be collected through virtually unlimited interacti… (voir plus)on with the environment, learning from limited interaction remains a key challenge. We posit that an agent can learn more efficiently if we augment reward maximization with self-supervised objectives based on structure in its visual input and sequential interaction with the environment. Our method, Momentum Predictive Representations (MPR), trains an agent to predict its own latent state representations multiple steps into the future. We compute target representations for future states using an encoder which is an exponential moving average of the agent's parameters, and we make predictions using a learned transition model. On its own, this future prediction objective outperforms prior methods for sample-efficient deep RL from pixels. We further improve performance by adding data augmentation to the future prediction loss, which forces the agent's representations to be consistent across multiple views of an observation. Our full self-supervised objective, which combines future prediction and data augmentation, achieves a median human-normalized score of 0.444 on Atari in a setting limited to 100K steps of environment interaction, which is a 66% relative improvement over the previous state-of-the-art. Moreover, even in this limited data regime, MPR exceeds expert human scores on 6 out of 26 games.
2020-07-12
arXiv.org (prépublication)
dblp.uni-trier.de
Data-Efficient Reinforcement Learning with Self-Predictive Representations
Max Schwarzer
Ankesh Anand
Rishab Goel
(Rex) Devon Hjelm
Aaron Courville
Philip Bachman
While deep reinforcement learning excels at solving tasks where large amounts of data can be collected through virtually unlimited interacti… (voir plus)on with the environment, learning from limited interaction remains a key challenge. We posit that an agent can learn more efficiently if we augment reward maximization with self-supervised objectives based on structure in its visual input and sequential interaction with the environment. Our method, Self-Predictive Representations(SPR), trains an agent to predict its own latent state representations multiple steps into the future. We compute target representations for future states using an encoder which is an exponential moving average of the agent's parameters and we make predictions using a learned transition model. On its own, this future prediction objective outperforms prior methods for sample-efficient deep RL from pixels. We further improve performance by adding data augmentation to the future prediction loss, which forces the agent's representations to be consistent across multiple views of an observation. Our full self-supervised objective, which combines future prediction and data augmentation, achieves a median human-normalized score of 0.415 on Atari in a setting limited to 100k steps of environment interaction, which represents a 55% relative improvement over the previous state-of-the-art. Notably, even in this limited data regime, SPR exceeds expert human scores on 7 out of 26 games. The code associated with this work is available at https://github.com/mila-iqia/spr
2020-07-12
ArXiv (prépublication)
arxiv.org
arxiv.org
Vision-Based Goal-Conditioned Policies for Underwater Navigation in the Presence of Obstacles
Travis Manderson
Juan Higuera
Stefan Wapnick
Jean-François Tremblay
Florian Shkurti
David Meger
Gregory Dudek
We present Nav2Goal, a data-efficient and end-to-end learning method for goal-conditioned visual navigation. Our technique is used to train … (voir plus)a navigation policy that enables a robot to navigate close to sparse geographic waypoints provided by a user without any prior map, all while avoiding obstacles and choosing paths that cover user-informed regions of interest. Our approach is based on recent advances in conditional imitation learning. General-purpose, safe and informative actions are demonstrated by a human expert. The learned policy is subsequently extended to be goal-conditioned by training with hindsight relabelling, guided by the robot's relative localization system, which requires no additional manual annotation. We deployed our method on an underwater vehicle in the open ocean to collect scientifically relevant data of coral reefs, which allowed our robot to operate safely and autonomously, even at very close proximity to the coral. Our field deployments have demonstrated over a kilometer of autonomous visual navigation, where the robot reaches on the order of 40 waypoints, while collecting scientifically relevant data. This is done while travelling within 0.5 m altitude from sensitive corals and exhibiting significant learned agility to overcome turbulent ocean conditions and to actively avoid collisions.
2020-07-12
Robotics: Science and Systems XVI (publié)
doi.org
arxiv.org
Material for IEEE Software paper "How Do Open Source Software Contributors Perceive and Address Usability?"
Wenting Wang
Jinghui Cheng
Jin Guo
2020-07-10
(publié)
doi.org
Attenuated Anticipation of Social and Monetary Rewards in Autism Spectrum Disorders
Sarah Baumeister
Carolin Moessnang
Nico Bast
Sarah Hohmann
Julian Tillmann
David Goyard
Tony Charman
Sara Ambrosino
Simon Baron-Cohen
Christian Beckmann
Sven Bölte
Thomas Bourgeron
Annika Rausch
Daisy Crawley
Flavio Dell’Acqua
Guillaume Dumas
Sarah Durston
Christine Ecker
Dorothea L. Floris
Vincent Frouin … (voir 19 de plus)
Hannah Hayward
Rosemary Holt
Mark Johnson
Emily J. H. Jones
Meng-Chuan Lai
Michael V. Lombardo
Luke Mason
Marianne Oldehinkel
Tony Persico
Antonia San José Cáceres
Thomas Wolfers
Will Spooren
Eva Loth
Declan Murphy
Jan K. Buitelaar
Heike Tost
Andreas Meyer-Lindenberg
Tobias Banaschewski
Daniel Brandeis
Background Reward processing has been proposed to underpin atypical social behavior, a core feature of autism spectrum disorder (ASD). Howev… (voir plus)er, previous neuroimaging studies have yielded inconsistent results regarding the specificity of atypicalities for social rewards in ASD. Utilizing a large sample, we aimed to assess altered reward processing in response to reward type (social, monetary) and reward phase (anticipation, delivery) in ASD. Methods Functional magnetic resonance imaging during social and monetary reward anticipation and delivery was performed in 212 individuals with ASD (7.6-30.5 years) and 181 typically developing (TD) participants (7.6-30.8 years). Results Across social and monetary reward anticipation, whole-brain analyses (p0.05, family-wise error-corrected) showed hypoactivation of the right ventral striatum (VS) in ASD. Further, region of interest (ROI) analy
2020-07-06
bioRxiv (prépublication)
doi.org
Deep interpretability for GWAS
Deepak Sharma
Audrey Durand
Marc-André Legault
Louis-philippe Lemieux Perreault
Audrey Lemaccon
Marie-Pierre Dub'e
Joelle Pineau
Genome-Wide Association Studies are typically conducted using linear models to find genetic variants associated with common diseases. In the… (voir plus)se studies, association testing is done on a variant-by-variant basis, possibly missing out on non-linear interaction effects between variants. Deep networks can be used to model these interactions, but they are difficult to train and interpret on large genetic datasets. We propose a method that uses the gradient based deep interpretability technique named DeepLIFT to show that known diabetes genetic risk factors can be identified using deep models along with possibly novel associations.
2020-07-03
ArXiv (prépublication)
arxiv.org
arxiv.org
Software Engineering Event Modeling using Relative Time in Temporal Knowledge Graphs
Kian Ahrabian
Daniel Tarlow
Hehuimin Cheng
Jin Guo
We present a multi-relational temporal Knowledge Graph based on the daily interactions between artifacts in GitHub, one of the largest socia… (voir plus)l coding platforms. Such representation enables posing many user-activity and project management questions as link prediction and time queries over the knowledge graph. In particular, we introduce two new datasets for i) interpolated time-conditioned link prediction and ii) extrapolated time-conditioned link/time prediction queries, each with distinguished properties. Our experiments on these datasets highlight the potential of adapting knowledge graphs to answer broad software engineering questions. Meanwhile, it also reveals the unsatisfactory performance of existing temporal models on extrapolated queries and time prediction queries in general. To overcome these shortcomings, we introduce an extension to current temporal models using relative temporal information with regards to past events.
2020-07-02
ArXiv (prépublication)
arxiv.org
arxiv.org
Compositional Generalization by Factorizing Alignment and Translation
Jacob Russin
Jason Jo
R. O’Reilly
Yoshua Bengio
2020-07-01
Annual Meeting of the Association for Computational Linguistics (publié)
doi.org
Counterexamples on the Monotonicity of Delay Optimal Strategies for Energy Harvesting Transmitters
Borna Sayedana
Aditya Mahajan
We consider cross-layer design of delay optimal transmission strategies for energy harvesting transmitters where the data and energy arrival… (voir plus) processes are stochastic. Using Markov decision theory, we show that the value function is weakly increasing in the queue state and weakly decreasing in the battery state. It is natural to expect that the delay optimal policy should be weakly increasing in the queue and battery states. We show via counterexamples that this is not the case. In fact, we show that for some sample scenarios the delay optimal policy may perform 5–13% better than the best monotone policy.
2020-07-01
IEEE Wireless Communications Letters (publié)
doi.org
arxiv.org
Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach
Wenyu Du
Zhouhan Lin
Yikang Shen
Timothy O'Donnell
Yoshua Bengio
Yue Sara Zhang
It is commonly believed that knowledge of syntactic structure should improve language modeling. However, effectively and computationally eff… (voir plus)iciently incorporating syntactic structure into neural language models has been a challenging topic. In this paper, we make use of a multi-task objective, i.e., the models simultaneously predict words as well as ground truth parse trees in a form called “syntactic distances”, where information between these two separate objectives shares the same intermediate representation. Experimental results on the Penn Treebank and Chinese Treebank datasets show that when ground truth parse trees are provided as additional training signals, the model is able to achieve lower perplexity and induce trees with better quality.
2020-07-01
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (publié)
doi.org
arxiv.org