Publications

View-Invariant Loop Closure with Oriented Semantic Landmarks
Jimmy Li
Karim Koreitem
David Meger
Gregory Dudek
Recent work on semantic simultaneous localization and mapping (SLAM) have shown the utility of natural objects as landmarks for improving lo… (voir plus)calization accuracy and robustness. In this paper we present a monocular semantic SLAM system that uses object identity and inter-object geometry for view-invariant loop detection and drift correction. Our system's ability to recognize an area of the scene even under large changes in viewing direction allows it to surpass the mapping accuracy of ORB-SLAM, which uses only local appearance-based features that are not robust to large viewpoint changes. Experiments on real indoor scenes show that our method achieves mean drift reduction of 70% when compared directly to ORB-SLAM. Additionally, we propose a method for object orientation estimation, where we leverage the tracked pose of a moving camera under the SLAM setting to overcome ambiguities caused by object symmetry. This allows our SLAM system to produce geometrically detailed semantic maps with object orientation, translation, and scale.
2019-12-31
IEEE International Conference on Robotics and Automation (publié)
doi.org
You could have said that instead: Improving Chatbots with Natural Language Feedback
Makesh Narsimhan Sreedhar
Kun Ni
Siva Reddy
The ubiquitous nature of dialogue systems and their interaction with users generate an enormous amount of data. Can we improve chatbots usin… (voir plus)g this data? A self-feeding chatbot improves itself by asking natural language feedback when a user is dissatisfied with its response and uses this feedback as an additional training sample. However, user feedback in most cases contains extraneous sequences hindering their usefulness as a training sample. In this work, we propose a generative adversarial model that converts noisy feedback into a plausible natural response in a conversation. The generator’s goal is to convert the feedback into a response that answers the user’s previous utterance and to fool the discriminator which distinguishes feedback from natural responses. We show that augmenting original training data with these modified feedback responses improves the original chatbot performance from 69.94%to 75.96% in ranking correct responses on the PERSONACHATdataset, a large improvement given that the original model is already trained on 131k samples.
2019-12-31
Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
arxiv.org
Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling
Tong Che
Ruixiang Zhang
Jascha Sohl-Dickstein
Hugo Larochelle
Liam Paull
Yuan Cao
Yoshua Bengio
We show that the sum of the implicit generator log-density …
2019-12-31
Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (publié)
doi.org
arxiv.org
Learning from Learning Machines: Optimisation, Rules, and Social Norms
Travis LaCroix
Yoshua Bengio
There is an analogy between machine learning systems and economic entities in that they are both adaptive, and their behaviour is specified … (voir plus)in a more-or-less explicit way. It appears that the area of AI that is most analogous to the behaviour of economic entities is that of morally good decision-making, but it is an open question as to how precisely moral behaviour can be achieved in an AI system. This paper explores the analogy between these two complex systems, and we suggest that a clearer understanding of this apparent analogy may help us forward in both the socio-economic domain and the AI domain: known results in economics may help inform feasible solutions in AI safety, but also known results in AI may inform economic policy. If this claim is correct, then the recent successes of deep learning for AI suggest that more implicit specifications work better than explicit ones for solving such problems.
2019-12-28
ArXiv (prépublication)
arxiv.org
arxiv.org
Assessment of Extubation Readiness Using Spontaneous Breathing Trials in Extremely Preterm Neonates
Wissam Shalish
Lara Kanbar
Lajos Kovacs
Sanjay Chawla
Martin Keszler
Smita Rao
Samantha Latremouille
Doina Precup
Karen Brown
Robert E. Kearney
Guilherme M. Sant’Anna
Importance Spontaneous breathing trials (SBTs) are used to determine extubation readiness in extremely preterm neonates (gestational age ≤… (voir plus)28 weeks), but these trials rely on empirical combinations of clinical events during endotracheal continuous positive airway pressure (ET-CPAP). Objectives To describe clinical events during ET-CPAP and to assess accuracy of comprehensive clinical event combinations in predicting successful extubation compared with clinical judgment alone. Design, Setting, and Participants This multicenter diagnostic study used data from 259 neonates seen at 5 neonatal intensive care units from the prospective Automated Prediction of Extubation Readiness (APEX) study from September 1, 2013, through August 31, 2018. Neonates with birth weight less than 1250 g who required mechanical ventilation were eligible. Neonates deemed to be ready for extubation and who underwent ET-CPAP before extubation were included. Interventions In the APEX study, cardiorespiratory signals were recorded during 5-minute ET-CPAP, and signs of clinical instability were monitored. Main Outcomes and Measures Four clinical events were documented during ET-CPAP: apnea requiring stimulation, presence and cumulative durations of bradycardia and desaturation, and increased supplemental oxygen. Clinical event occurrence was assessed and compared between extubation pass and fail (defined as reintubation within 7 days). An automated algorithm was developed to generate SBT definitions using all clinical event combinations and to compute diagnostic accuracies of an SBT in predicting extubation success. Results Of 259 neonates (139 [54%] male) with a median gestational age of 26.1 weeks (interquartile range [IQR], 24.9-27.4 weeks) and median birth weight of 830 g (IQR, 690-1019 g), 147 (57%) had at least 1 clinical event during ET-CPAP. Apneas occurred in 10% (26 of 259) of neonates, bradycardias in 19% (48), desaturations in 53% (138), and increased oxygen needs in 41% (107). Neonates with successful extubation (71% [184 of 259]) had significantly fewer clinical events (51% [93 of 184] vs 72% [54 of 75], P = .002), shorter cumulative bradycardia duration (median, 0 seconds [IQR, 0 seconds] vs 0 seconds [IQR, 0-9 seconds], P  .001), shorter cumulative desaturation duration (median, 0 seconds [IQR, 0-59 seconds] vs 25 seconds [IQR, 0-90 seconds], P = .003),
2019-12-19
JAMA Pediatrics (publié)
doi.org
CLOSURE: Assessing Systematic Generalization of CLEVR Models
Dzmitry Bahdanau
Harm de Vries
Timothy J. O'Donnell
Shikhar Murty
Philippe Beaudoin
Yoshua Bengio
Aaron Courville
The CLEVR dataset of natural-looking questions about 3D-rendered scenes has recently received much attention from the research community. A … (voir plus)number of models have been proposed for this task, many of which achieved very high accuracies of around 97-99%. In this work, we study how systematic the generalization of such models is, that is to which extent they are capable of handling novel combinations of known linguistic constructs. To this end, we test models' understanding of referring expressions based on matching object properties (such as e.g. "the object that is the same size as the red ball") in novel contexts. Our experiments on the thereby constructed CLOSURE benchmark show that state-of-the-art models often do not exhibit systematicity after being trained on CLEVR. Surprisingly, we find that an explicitly compositional Neural Module Network model also generalizes badly on CLOSURE, even when it has access to the ground-truth programs at test time. We improve the NMN's systematic generalization by developing a novel Vector-NMN module architecture with vector-valued inputs and outputs. Lastly, we investigate the extent to which few-shot transfer learning can help models that are pretrained on CLEVR to adapt to CLOSURE. Our few-shot learning experiments contrast the adaptation behavior of the models with intermediate discrete programs with that of the end-to-end continuous models.
2019-12-11
ArXiv (prépublication)
doi.org
arxiv.org
Shaping representations through communication: community size effect in artificial learning systems
Olivier Tieleman
Angeliki Lazaridou
Shibl Mourad
Charles Blundell
Doina Precup
Motivated by theories of language and communication that explain why communities with large numbers of speakers have, on average, simpler la… (voir plus)nguages with more regularity, we cast the representation learning problem in terms of learning to communicate. Our starting point sees the traditional autoencoder setup as a single encoder with a fixed decoder partner that must learn to communicate. Generalizing from there, we introduce community-based autoencoders in which multiple encoders and decoders collectively learn representations by being randomly paired up on successive training iterations. We find that increasing community sizes reduce idiosyncrasies in the learned codes, resulting in representations that better encode concept categories and correlate with human feature norms.
2019-12-11
ArXiv (prépublication)
arxiv.org
arxiv.org
Entropy Regularization with Discounted Future State Distribution in Policy Gradient Methods
Riashat Islam
Raihan Seraj
Pierre-Luc Bacon
Doina Precup
The policy gradient theorem is defined based on an objective with respect to the initial distribution over states. In the discounted case, t… (voir plus)his results in policies that are optimal for one distribution over initial states, but may not be uniformly optimal for others, no matter where the agent starts from. Furthermore, to obtain unbiased gradient estimates, the starting point of the policy gradient estimator requires sampling states from a normalized discounted weighting of states. However, the difficulty of estimating the normalized discounted weighting of states, or the stationary state distribution, is quite well-known. Additionally, the large sample complexity of policy gradient methods is often attributed to insufficient exploration, and to remedy this, it is often assumed that the restart distribution provides sufficient exploration in these algorithms. In this work, we propose exploration in policy gradient methods based on maximizing entropy of the discounted future state distribution. The key contribution of our work includes providing a practically feasible algorithm to estimate the normalized discounted weighting of states, i.e, the \textit{discounted future state distribution}. We propose that exploration can be achieved by entropy regularization with the discounted state distribution in policy gradients, where a metric for maximal coverage of the state space can be based on the entropy of the induced state distribution. The proposed approach can be considered as a three time-scale algorithm and under some mild technical conditions, we prove its convergence to a locally optimal policy. Experimentally, we demonstrate usefulness of regularization with the discounted future state distribution in terms of increased state space coverage and faster learning on a range of complex tasks.
2019-12-10
ArXiv (prépublication)
arxiv.org
arxiv.org
Marginalized State Distribution Entropy Regularization in Policy Optimization
Riashat Islam
Zafarali Ahmed
Doina Precup
2019-12-10
ArXiv (prépublication)
arxiv.org
arxiv.org
Doubly Robust Off-Policy Actor-Critic Algorithms for Reinforcement Learning
Riashat Islam
Raihan Seraj
Samin Yeasar Arnob
Doina Precup
We study the problem of off-policy critic evaluation in several variants of value-based off-policy actor-critic algorithms. Off-policy actor… (voir plus)-critic algorithms require an off-policy critic evaluation step, to estimate the value of the new policy after every policy gradient update. Despite enormous success of off-policy policy gradients on control tasks, existing general methods suffer from high variance and instability, partly because the policy improvement depends on gradient of the estimated value function. In this work, we present a new way of off-policy policy evaluation in actor-critic, based on the doubly robust estimators. We extend the doubly robust estimator from off-policy policy evaluation (OPE) to actor-critic algorithms that consist of a reward estimator performance model. We find that doubly robust estimation of the critic can significantly improve performance in continuous control tasks. Furthermore, in cases where the reward function is stochastic that can lead to high variance, doubly robust critic estimation can improve performance under corrupted, stochastic reward signals, indicating its usefulness for robust and safe reinforcement learning.
2019-12-09
arXiv (prépublication)
doi.org
arxiv.org
Interactive Psychometrics for Autism with the Human Dynamic Clamp: Interpersonal Synchrony from Sensory-motor to Socio-cognitive Domains
Florence Baillin
Aline Lefebvre
Amandine Pedoux
Yann Beauxis
Denis-Alexander Engemann
Anna Maruani
Frederique Amsellem
Thomas Bourgeron
Richard Delorme
Guillaume Dumas
2019-12-05
(publié)
doi.org
Mutations associated with neuropsychiatric conditions delineate functional brain connectivity dimensions contributing to autism and schizophrenia
Clara A. Moreau
Sebastian G. W. Urchs
Pierre Orban
Catherine Schramm
Guillaume Dumas
Aurélie Labbe
Guillaume Huguet
Élise Douard
Pierre-Olivier Quirion
Amy Lin
Leila Kushan
Stephanie Grot
David Luck
Adrianna Mendrek
Stephane Potvin
Emmanuel Stip
Thomas Bourgeron
Alan C. Evans
Carrie E. Bearden
Pierre Bellec … (voir 1 de plus)
Sébastien Jacquemont
16p11.2 and 22q11.2 Copy Number Variants (CNVs) confer high risk for Autism Spectrum Disorder (ASD), schizophrenia (SZ), and Attention-Defic… (voir plus)it-Hyperactivity-Disorder (ADHD), but their impact on functional connectivity (FC) remains unclear. Here we report an analysis of resting-state FC using magnetic resonance imaging data from 101 CNV carriers, 755 individuals with idiopathic ASD, SZ, or ADHD and 1,072 controls. We characterize CNV FC-signatures and use them to identify dimensions contributing to complex idiopathic conditions. CNVs have large mirror effects on FC at the global and regional level. Thalamus, somatomotor, and posterior insula regions play a critical role in dysconnectivity shared across deletions, duplications, idiopathic ASD, SZ but not ADHD. Individuals with higher similarity to deletion FC-signatures exhibit worse cognitive and behavioral symptoms. Deletion similarities identified at the connectivity level could be related to the redundant associations observed genome-wide between gene expression spatial patterns and FC-signatures. Results may explain why many CNVs affect a similar range of neuropsychiatric symptoms.
2019-12-05
bioRxiv (prépublication)
doi.org