Publications

Combating False Negatives in Adversarial Imitation Learning (Student Abstract)
Konrad Żołna
Chitwan Saharia
Léonard Boussioux
David Y. T. Hui
Maxime Chevalier-Boisvert
Dzmitry Bahdanau
Yoshua Bengio
2020-04-02
AAAI Conference on Artificial Intelligence (publié)
doi.org
Detecting semantic anomalies
Faruk Ahmed
Aaron Courville
We critically appraise the recent interest in out-of-distribution (OOD) detection and question the practical relevance of existing benchmark… (voir plus)s. While the currently prevalent trend is to consider different datasets as OOD, we argue that out-distributions of practical interest are ones where the distinction is semantic in nature for a specified context, and that evaluative tasks should reflect this more closely. Assuming a context of object recognition, we recommend a set of benchmarks, motivated by practical applications. We make progress on these benchmarks by exploring a multi-task learning based approach, showing that auxiliary objectives for improved semantic awareness result in improved semantic anomaly detection, with accompanying generalization benefits.
2020-04-02
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
Gifting in Multi-Agent Reinforcement Learning (Student Abstract)
Andrei Lupu
Doina Precup
This work performs a first study on multi-agent reinforcement learning with deliberate reward passing between agents. We empirically demonst… (voir plus)rate that such mechanics can greatly improve the learning progression in a resource appropriation setting and provide a preliminary discussion of the complex effects of gifting on the learning dynamics.
2020-04-02
AAAI Conference on Artificial Intelligence (publié)
doi.org
Literature Mining for Incorporating Inductive Bias in Biomedical Prediction Tasks (Student Abstract)
Qizhen Zhang
Audrey Durand
Joelle Pineau
2020-04-02
AAAI Conference on Artificial Intelligence (publié)
doi.org
Modeling Dialogues with Hashcode Representations: A Nonparametric Approach
Sahil Garg
Irina Rish
Guillermo Cecchi
Palash Goyal
Shuyang Gao
Sarik Ghazarian
Greg Ver Steeg
Aram Galstyan
We propose a novel dialogue modeling framework, the first-ever nonparametric kernel functions based approach for dialogue modeling, which le… (voir plus)arns hashcodes as text representations; unlike traditional deep learning models, it handles well relatively small datasets, while also scaling to large ones. We also derive a novel lower bound on mutual information, used as a model-selection criterion favoring representations with better alignment between the utterances of participants in a collaborative dialogue setting, as well as higher predictability of the generated responses. As demonstrated on three real-life datasets, including prominently psychotherapy sessions, the proposed approach significantly outperforms several state-of-art neural network based dialogue systems, both in terms of computational efficiency, reducing training time from days or weeks to hours, and the response quality, achieving an order of magnitude improvement over competitors in frequency of being chosen as the best model by human evaluators.
2020-04-02
AAAI Conference on Artificial Intelligence (publié)
doi.org
Options of Interest: Temporal Abstraction with Interest Functions
Khimya Khetarpal
Martin Klissarov
Maxime Chevalier-Boisvert
Pierre-Luc Bacon
Doina Precup
Temporal abstraction refers to the ability of an agent to use behaviours of controllers which act for a limited, variable amount of time. Th… (voir plus)e options framework describes such behaviours as consisting of a subset of states in which they can initiate, an internal policy and a stochastic termination condition. However, much of the subsequent work on option discovery has ignored the initiation set, because of difficulty in learning it from data. We provide a generalization of initiation sets suitable for general function approximation, by defining an interest function associated with an option. We derive a gradient-based learning algorithm for interest functions, leading to a new interest-option-critic architecture. We investigate how interest functions can be leveraged to learn interpretable and reusable temporal abstractions. We demonstrate the efficacy of the proposed approach through quantitative and qualitative results, in both discrete and continuous environments.
2020-04-02
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning
Dayiheng Liu
Jie Fu
Yidan Zhang
Christopher Pal
Jiancheng Lv
Typical methods for unsupervised text style transfer often rely on two key ingredients: 1) seeking the explicit disentanglement of the conte… (voir plus)nt and the attributes, and 2) troublesome adversarial learning. In this paper, we show that neither of these components is indispensable. We propose a new framework that utilizes the gradients to revise the sentence in a continuous space during inference to achieve text style transfer. Our method consists of three key components: a variational auto-encoder (VAE), some attribute predictors (one for each attribute), and a content predictor. The VAE and the two types of predictors enable us to perform gradient-based optimization in the continuous space, which is mapped from sentences in a discrete space, to find the representation of a target sentence with the desired attributes and preserved content. Moreover, the proposed method naturally has the ability to simultaneously manipulate multiple fine-grained attributes, such as sentence length and the presence of specific words, when performing text style transfer tasks. Compared with previous adversarial learning based methods, the proposed method is more interpretable, controllable and easier to train. Extensive experimental studies on three popular text style transfer tasks show that the proposed method significantly outperforms five state-of-the-art methods.
2020-04-02
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
Learning to cooperate: Emergent communication in multi-agent navigation
Ivana Kaji'c
Eser Aygün
Doina Precup
Emergent communication in artificial agents has been studied to understand language evolution, as well as to develop artificial systems that… (voir plus) learn to communicate with humans. We show that agents performing a cooperative navigation task in various gridworld environments learn an interpretable communication protocol that enables them to efficiently, and in many cases, optimally, solve the task. An analysis of the agents' policies reveals that emergent signals spatially cluster the state space, with signals referring to specific locations and spatial directions such as "left", "up", or "upper left room". Using populations of agents, we show that the emergent protocol has basic compositional structure, thus exhibiting a core property of natural language.
2020-04-01
ArXiv (prépublication)
arxiv.org
arxiv.org
GitHub Repositories with Links to Academic Papers: Open Access, Traceability, and Evolution
Supatsara Wattanakriengkrai
Bodin Chinthanet
Hideaki Hata
Raula Gaikovina Kula
Christoph Treude
Jin L.C. Guo
Ken-ichi Matsumoto
Traceability between published scientific breakthroughs and their implementation is essential, especially in the case of Open Source Softwar… (voir plus)e implements bleeding edge science into its code. However, aligning the link between GitHub repositories and academic papers can prove difficult, and the link impact remains unknown. This paper investigates the role of academic paper references contained in these repositories. We conducted a large-scale study of 20 thousand GitHub repositories to establish prevalence of references to academic papers. We use a mixed-methods approach to identify Open Access (OA), traceability and evolutionary aspects of the links. Although referencing a paper is not typical, we find that a vast majority of referenced academic papers are OA. In terms of traceability, our analysis revealed that machine learning is the most prevalent topic of repositories. These repositories tend to be affiliated with academic communities. More than half of the papers do not link back to any repository. A case study of referenced arXiv paper shows that most of these papers are high-impact and influential and do align with academia, referenced by repositories written in different programming languages. From the evolutionary aspect, we find very few changes of papers being referenced and links to them.
2020-03-31
arXiv.org (prépublication)
dblp.uni-trier.de
Multi-language design smells: a backstage perspective
Mouna Abidi
Md Saidur Rahman
Moses Openja
Foutse Khomh
2020-03-31
Empirical Software Engineering (publié)
doi.org
Pruning for efficient hardware implementations of deep neural networks
Ghouthi Boukli hacene
Vincent Gripon
Matthieu Arzel
Nicolas Farrugia
Yoshua Bengio
2020-03-31
(publié)
www.semanticscholar.org
Both New and Chronic Potentially Inappropriate Medications Continued at Hospital Discharge Are Associated With Increased Risk of Adverse Events
Daniala L. Weir
Todd C. Lee
Emily G. McDonald
Aude Motulsky
Michal Abrahamowicz
Steven Morgan
David Buckeridge
Robyn Tamblyn
Admission to hospital provides the opportunity to review patient medications; however, the extent to which the safety of drug regimens chang… (voir plus)es after hospitalization is unclear. To estimate the number of potentially inappropriate medications (PIMs) prescribed to patients at hospital discharge and their association with the risk of adverse events 30 days after discharge. Prospective cohort study. Tertiary care hospitals within the McGill University Health Centre Network in Montreal, Quebec, Canada. Patients from internal medicine, cardiac, and thoracic surgery, aged 65 years and older, admitted between October 2014 and November 2016. Abstracted chart data were linked to provincial health databases. PIMs were identified using AGS (American Geriatrics Society) Beers Criteria®, STOPP, and Choosing Wisely statements. Multivariable logistic regression and Cox models were used to assess the association between PIMs and adverse events. Of 2,402 included patients, 1,381 (57%) were male; median age was 76 years (interquartile range [IQR] = 70‐82 years); and eight discharge medications were prescribed (IQR = 2‐8). A total of 1,576 (66%) patients were prescribed at least one PIM at discharge; 1,176 (49%) continued a PIM from prior to admission, and 755 (31%) were prescribed at least one new PIM. In the 30 days after discharge, 218 (9%) experienced an adverse drug event (ADE) and 862 (36%) visited the emergency department (ED), were rehospitalized, or died. After adjustment, each additional new PIM and continued community PIM were respectively associated with a 21% (odds ratio [OR] = 1.21; 95% confidence interval [CI] = 1.01‐1.45) and a 10% (OR = 1.10; 95% CI = 1.01‐1.21) increased odds of ADEs. They were also respectively associated with a 13% (hazard ratio [HR] = 1.13; 95% CI = 1.03‐1.26) and a 5% (HR = 1.05; 95% CI = 1.00‐1.10) increased risk of ED visits, rehospitalization, and death. Two in three hospitalized patients were prescribed a PIM at discharge, and increasing numbers of PIMs were associated with an increased risk of ADEs and all‐cause adverse events. Improving hospital prescribing practices may reduce the frequency of PIMs and associated adverse events. J Am Geriatr Soc 68:1184–1192, 2020.
2020-03-30
Journal of the American Geriatrics Society (publié)
doi.org