Publications

How Do Open Source Software Contributors Perceive and Address Usability?: Valued Factors, Practices, and Challenges
Wenting Wang
Jinghui Cheng
Jin L.C. Guo
Given the recent changes in the open source software (OSS) landscape, we examined OSS contributors’ current valued factors, practices, and… (see more) challenges concerning usability. Our survey provides insights for OSS practitioners and tool designers to promote a user-centric mindset and improve usability practice in OSS communities.
2022-01-31
IEEE Software (published)
doi.org
arxiv.org
Improving Sample Efficiency of Value Based Models Using Attention and Vision Transformers
Amir Ardalan Kalantari
Mohammad Saeed Amini
A. Chandar
Doina Precup
Much of recent Deep Reinforcement Learning success is owed to the neural architecture's potential to learn and use effective internal repres… (see more)entations of the world. While many current algorithms access a simulator to train with a large amount of data, in realistic settings, including while playing games that may be played against people, collecting experience can be quite costly. In this paper, we introduce a deep reinforcement learning architecture whose purpose is to increase sample efficiency without sacrificing performance. We design this architecture by incorporating advances achieved in recent years in the field of Natural Language Processing and Computer Vision. Specifically, we propose a visually attentive model that uses transformers to learn a self-attention mechanism on the feature maps of the state representation, while simultaneously optimizing return. We demonstrate empirically that this architecture improves sample complexity for several Atari environments, while also achieving better performance in some of the games.
2022-01-31
ArXiv (preprint)
arxiv.org
arxiv.org
Exploring social inequalities in healthcare trajectories following diagnosis of diabetes: a state sequence analysis of linked survey and administrative data
Rachel McKay
Laurence Letarte
Alain Gillian Lucie David Manon Catherine Anaïs Benoit Alexandre Amélie Pasquale Valérie Marie-Pascale Mike Anne-Marie Marc Josiane Mireille Stéphanie Pierre Annie Isabelle Danielle Denis Jaime André Geneviève Jean-François Roxanne Marc-Antoine Pier Sonia Vanasse
Alain Gillian Lucie David Manon Catherine Anaïs Benoit A Vanasse Bartlett Blais Buckeridge Choinière Hudon
Alain Vanasse
Gillian Bartlett
Lucie Blais
David L Buckeridge
Manon Choinière
Catherine Hudon
Anaïs Lacasse
Benoit Lamarche
Alexandre Lebel
Amélie Quesnel-Vallée
Pasquale Roberge
Valérie Émond
Marie-Pascale Pomey
Mike Benigeri
Anne-Marie Cloutier
Marc Dorais … (see 16 more)
Josiane Courteau
Mireille Courteau
Stéphanie Plante
Pierre Cambon
Annie Giguère
Isabelle Leroux
Danielle St-Laurent
Denis Roy
Jaime Borja
André Néron
Geneviève Landry
Jean-François Ethier
Roxanne Dault
Marc-Antoine Côté-Marcil
Pier Tremblay
Sonia Quirion
2022-01-30
BMC Health Services Research (published)
doi.org
Memory-Aware Functional IR for Higher-Level Synthesis of Accelerators
Christof Schlaak
Tzung-Han Juang
Christophe Dubach
2022-01-30
ACM Transactions on Architecture and Code Optimization (published)
doi.org
Sex-specific lesion pattern of functional outcomes after stroke
Anna K. Bonkhoff
Martin Bretzner
Sungmin Hong
Markus D. Schirmer
Alexander Cohen
Robert W. Regenhardt
Kathleen L. Donahue
Marco J. Nardin
Adrian V. Dalca
Anne-Katrin Giese
Mark R. Etherton
Brandon L. Hancock
Steven J. T. Mocking
Elissa C. McIntosh
John Attia
Oscar R. Benavente
Stephen Bevan
John W. Cole
Amanda Donatti
Christoph J. Griessenauer … (see 39 more)
Laura Heitsch
Lukas Holmegaard
Katarina Jood
Jordi Jimenez-Conde
Steven J. Kittner
Robin Lemmens
Christopher R. Levi
Caitrin W. McDonough
James F. Meschia
Chia-Ling Phuah
Arndt Rolfs
Stefan Ropele
Jonathan Rosand
Jaume Roquer
Tatjana Rundek
Ralph L. Sacco
Reinhold Schmidt
Pankaj Sharma
Agnieszka Slowik
Martin Söderholm
Alessandro Sousa
Tara M. Stanne
Daniel Strbian
Turgut Tatlisumak
Vincent Thijs
Achala Vagal
Johan Wasselius
Daniel Woo
Ramin Zand
Patrick F. McArdle
Bradford B. Worrall
Christina Jern
Arne G. Lindgren
Jane Maguire
Michael D. Fox
Danilo Bzdok
Ona Wu
Natalia S. Rost
Anna K. Martin Sungmin Markus D. Alexander Robert W. Kathleen L. Marco J. Adrian V. Anne-Katrin Mark R. Brandon L. Steven J. T. Elissa C. John Oscar R. Stephen John W. Amanda Christoph J. Laura Lukas Katarina Jordi Steven J. Robin Christopher R. Caitrin W. James F. Chia-Ling Arndt Stefan Jonathan Jaume Tatjana Ralph L. Reinhold Pankaj Agnieszka Martin Alessandro Tara M. Daniel Turgut Vincent Achala Johan Daniel Ramin Patrick F. Bradford B. Christina Arne G. Jane Michael D. Danilo Ona Natalia S. Bonkhoff
Stroke represents a considerable burden of disease for both men and women. However, a growing body of literature suggests clinically relevan… (see more)t sex differences in the underlying causes, presentations and outcomes of acute ischaemic stroke. In a recent study, we reported sex divergences in lesion topographies: specific to women, acute stroke severity was linked to lesions in the left-hemispheric posterior circulation. We here determined whether these sex-specific brain manifestations also affect long-term outcomes. We relied on 822 acute ischaemic patients [age: 64.7 (15.0) years, 39% women] originating from the multi-centre MRI-GENIE study to model unfavourable outcomes (modified Rankin Scale >2) based on acute neuroimaging data in a Bayesian hierarchical framework. Lesions encompassing bilateral subcortical nuclei and left-lateralized regions in proximity to the insula explained outcomes across men and women (area under the curve = 0.81). A pattern of left-hemispheric posterior circulation brain regions, combining left hippocampus, precuneus, fusiform and lingual gyrus, occipital pole and latero-occipital cortex, showed a substantially higher relevance in explaining functional outcomes in women compared to men [mean difference of Bayesian posterior distributions (men – women) = −0.295 (90% highest posterior density interval = −0.556 to −0.068)]. Once validated in prospective studies, our findings may motivate a sex-specific approach to clinical stroke management and hold the promise of enhancing outcomes on a population level.
2022-01-30
Brain Communications (published)
doi.org
ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods
Victor Schmidt
Alexandra Luccioni
Mélisande Teng
Tianyu Zhang
Alexia Reynaud
Sunand Raghupathi
Gautier Cosne
Adrien Juraver
Vahe Vardanyan
Alex Hernandez-Garcia
Yoshua Bengio
Climate change is a major threat to humanity, and the actions required to prevent its catastrophic consequences include changes in both poli… (see more)cy-making and individual behaviour. However, taking action requires understanding the effects of climate change, even though they may seem abstract and distant. Projecting the potential consequences of extreme climate events such as flooding in familiar places can help make the abstract impacts of climate change more concrete and encourage action. As part of a larger initiative to build a website that projects extreme climate events onto user-chosen photos, we present our solution to simulate photo-realistic floods on authentic images. To address this complex task in the absence of suitable training data, we propose ClimateGAN, a model that leverages both simulated and real data for unsupervised domain adaptation and conditional image generation. In this paper, we describe the details of our framework, thoroughly evaluate components of our architecture and demonstrate that our model is capable of robustly generating photo-realistic flooding.
2022-01-27
ICLR.cc/2022/Conference (poster)
doi.org
openreview.net
Conditional Image Generation by Conditioning Variational Auto-Encoders
William Harvey
Saeid Naderiparizi
Frank N. Wood
We present a conditional variational auto-encoder (VAE) which, to avoid the substantial cost of training from scratch, uses an architecture … (see more)and training objective capable of leveraging a foundation model in the form of a pretrained unconditional VAE. To train the conditional VAE, we only need to train an artifact to perform amortized inference over the unconditional VAE's latent variables given a conditioning input. We demonstrate our approach on tasks including image inpainting, for which it outperforms state-of-the-art GAN-based approaches at faithfully representing the inherent uncertainty. We conclude by describing a possible application of our inpainting model, in which it is used to perform Bayesian experimental design for the purpose of guiding a sensor.
2022-01-27
ICLR.cc/2022/Conference (poster)
openreview.net
openreview.net
Constructing a Good Behavior Basis for Transfer Using Generalized Policy Updates
Safa Alver
Doina Precup
We study the problem of learning a good set of policies, so that when combined together, they can solve a wide variety of unseen reinforceme… (see more)nt learning tasks with no or very little new data. Specifically, we consider the framework of generalized policy evaluation and improvement, in which the rewards for all tasks of interest are assumed to be expressible as a linear combination of a fixed set of features. We show theoretically that, under certain assumptions, having access to a specific set of diverse policies, which we call a set of independent policies, can allow for instantaneously achieving high-level performance on all possible downstream tasks which are typically more complex than the ones on which the agent was trained. Based on this theoretical analysis, we propose a simple algorithm that iteratively constructs this set of policies. In addition to empirically validating our theoretical results, we compare our approach with recently proposed diverse policy set construction methods and show that, while others fail, our approach is able to build a behavior basis that enables instantaneous transfer to all possible downstream tasks. We also show empirically that having access to a set of independent policies can better bootstrap the learning process on downstream tasks where the new reward function cannot be described as a linear combination of the features. Finally, we demonstrate how this policy set can be useful in a lifelong reinforcement learning setting.
2022-01-27
ICLR.cc/2022/Conference (poster)
doi.org
openreview.net
Continuous-Time Meta-Learning with Forward Mode Differentiation
Tristan Deleu
David Kanaa
Leo Feng
Giancarlo Kerg
Yoshua Bengio
Guillaume Lajoie
Pierre-Luc Bacon
Drawing inspiration from gradient-based meta-learning methods with infinitely small gradient steps, we introduce Continuous-Time Meta-Learni… (see more)ng (COMLN), a meta-learning algorithm where adaptation follows the dynamics of a gradient vector field. Specifically, representations of the inputs are meta-learned such that a task-specific linear classifier is obtained as a solution of an ordinary differential equation (ODE). Treating the learning process as an ODE offers the notable advantage that the length of the trajectory is now continuous, as opposed to a fixed and discrete number of gradient steps. As a consequence, we can optimize the amount of adaptation necessary to solve a new task using stochastic gradient descent, in addition to learning the initial conditions as is standard practice in gradient-based meta-learning. Importantly, in order to compute the exact meta-gradients required for the outer-loop updates, we devise an efficient algorithm based on forward mode differentiation, whose memory requirements do not scale with the length of the learning trajectory, thus allowing longer adaptation in constant memory. We provide analytical guarantees for the stability of COMLN, we show empirically its efficiency in terms of runtime and memory usage, and we illustrate its effectiveness on a range of few-shot image classification problems.
2022-01-27
ICLR.cc/2022/Conference (spotlight)
doi.org
openreview.net
Coordination Among Neural Modules Through a Shared Global Workspace
Anirudh Goyal
Aniket Didolkar
Alex Lamb
Kartikeya Badola
Nan Rosemary Ke
Nasim Rahaman
Jonathan Binas
Charles Blundell
Michael Mozer
Yoshua Bengio
Deep learning has seen a movement away from representing examples with a monolithic hidden state towards a richly structured state. For exam… (see more)ple, Transformers segment by position, and object-centric architectures decompose images into entities. In all these architectures, interactions between different elements are modeled via pairwise interactions: Transformers make use of self-attention to incorporate information from other positions; object-centric architectures make use of graph neural networks to model interactions among entities. However, pairwise interactions may not achieve global coordination or a coherent, integrated representation that can be used for downstream tasks. In cognitive science, a global workspace architecture has been proposed in which functionally specialized components share information through a common, bandwidth-limited communication channel. We explore the use of such a communication channel in the context of deep learning for modeling the structure of complex environments. The proposed method includes a shared workspace through which communication among different specialist modules takes place but due to limits on the communication bandwidth, specialist modules must compete for access. We show that capacity limitations have a rational basis in that (1) they encourage specialization and compositionality and (2) they facilitate the synchronization of otherwise independent specialists.
2022-01-27
ICLR.cc/2022/Conference (oral)
doi.org
openreview.net
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation
Jongmin Lee
Cosmin Paduraru
Daniel J Mankowitz
Nicolas Heess
Doina Precup
Kee-Eung Kim
Arthur Guez
We consider the offline constrained reinforcement learning (RL) problem, in which the agent aims to compute a policy that maximizes expected… (see more) return while satisfying given cost constraints, learning only from a pre-collected dataset. This problem setting is appealing in many real-world scenarios, where direct interaction with the environment is costly or risky, and where the resulting policy should comply with safety constraints. However, it is challenging to compute a policy that guarantees satisfying the cost constraints in the offline RL setting, since the off-policy evaluation inherently has an estimation error. In this paper, we present an offline constrained RL algorithm that optimizes the policy in the space of the stationary distribution. Our algorithm, COptiDICE, directly estimates the stationary distribution corrections of the optimal policy with respect to returns, while constraining the cost upper bound, with the goal of yielding a cost-conservative policy for actual constraint satisfaction. Experimental results show that COptiDICE attains better policies in terms of constraint satisfaction and return-maximization, outperforming baseline algorithms.
2022-01-27
ICLR.cc/2022/Conference (spotlight)
doi.org
openreview.net
Deep ReLU Networks Preserve Expected Length
Boris Hanin
Ryan Jeong
David Rolnick
Assessing the complexity of functions computed by a neural network helps us understand how the network will learn and generalize. One natura… (see more)l measure of complexity is how the network distorts length - if the network takes a unit-length curve as input, what is the length of the resulting curve of outputs? It has been widely believed that this length grows exponentially in network depth. We prove that in fact this is not the case: the expected length distortion does not grow with depth, and indeed shrinks slightly, for ReLU networks with standard random initialization. We also generalize this result by proving upper bounds both for higher moments of the length distortion and for the distortion of higher-dimensional volumes. These theoretical results are corroborated by our experiments.
2022-01-27
ICLR.cc/2022/Conference (poster)
openreview.net
openreview.net