Publications

Literature Mining for Incorporating Inductive Bias in Biomedical Prediction Tasks (Student Abstract)
Qizhen Zhang
Audrey Durand
Joelle Pineau
2020-04-03
AAAI Conference on Artificial Intelligence (published)
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… (see more)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.
2020-04-03
AAAI Conference on Artificial Intelligence (published)
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… (see more)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.
2020-04-03
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.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 Guo
Ken-ichi Matsumoto
Traceability between published scientific breakthroughs and their implementation is essential, especially in the case of Open Source Softwar… (see more)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.
2020-04-01
arXiv.org (preprint)
dblp.uni-trier.de
Multi-language design smells: a backstage perspective
Mouna Abidi
Md. Saidur Rahman
Moses Openja
Foutse Khomh
2020-04-01
Empirical Software Engineering (published)
doi.org
Multi-language design smells: a backstage perspective
Mouna Abidi
Md Saidur Rahman
Moses Openja
Foutse Khomh
2020-04-01
Empirical Software Engineering (published)
doi.org
Bayesian latent multi‐state modeling for nonequidistant longitudinal electronic health records
Yu Luo
David A. Stephens
Aman Verma
David Buckeridge
2020-03-31
Biometrics (published)
doi.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
2020-03-31
Journal of The American Geriatrics Society (published)
doi.org
Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits
Alexandre Payeur
Jordan Guerguiev
Friedemann Zenke
Blake Richards
Richard Naud
2020-03-31
Nature Neuroscience (published)
doi.org
Towards Lifelong Self-Supervision For Unpaired Image-to-Image Translation
Victor Schmidt
Makesh Narsimhan Sreedhar
Mostafa ElAraby
Irina Rish
Unpaired Image-to-Image Translation (I2IT) tasks often suffer from lack of data, a problem which self-supervised learning (SSL) has recently… (see more) been very popular and successful at tackling. Leveraging auxiliary tasks such as rotation prediction or generative colorization, SSL can produce better and more robust representations in a low data regime. Training such tasks along an I2IT task is however computationally intractable as model size and the number of task grow. On the other hand, learning sequentially could incur catastrophic forgetting of previously learned tasks. To alleviate this, we introduce Lifelong Self-Supervision (LiSS) as a way to pre-train an I2IT model (e.g., CycleGAN) on a set of self-supervised auxiliary tasks. By keeping an exponential moving average of past encoders and distilling the accumulated knowledge, we are able to maintain the network's validation performance on a number of tasks without any form of replay, parameter isolation or retraining techniques typically used in continual learning. We show that models trained with LiSS perform better on past tasks, while also being more robust than the CycleGAN baseline to color bias and entity entanglement (when two entities are very close).
2020-03-31
ArXiv (preprint)
arxiv.org
arxiv.org
Planning as Inference in Epidemiological Models
Frank Wood
Andrew Warrington
Saeid Naderiparizi
Christian Dietrich Weilbach
Vaden Masrani
William Harvey
Adam Ścibior
Boyan Beronov
Seyed Ali Nasseri
In this work we demonstrate how existing software tools can be used to automate parts of infectious disease-control policy-making via perfor… (see more)ming inference in existing epidemiological dynamics models. The kind of inference tasks undertaken include computing, for planning purposes, the posterior distribution over putatively controllable, via direct policy-making choices, simulation model parameters that give rise to acceptable disease progression outcomes. Neither the full capabilities of such inference automation software tools nor their utility for planning is widely disseminated at the current time. Timely gains in understanding about these tools and how they can be used may lead to more fine-grained and less economically damaging policy prescriptions, particularly during the current COVID-19 pandemic.
2020-03-30
arXiv.org (preprint)
dblp.uni-trier.de
Planning as Inference in Epidemiological Models
Frank Wood
Andrew Warrington
Saeid Naderiparizi
Christian Dietrich Weilbach
Vaden Masrani
William Harvey
Adam Ścibior
Boyan Beronov
Seyed Ali Nasseri
In this work we demonstrate how existing software tools can be used to automate parts of infectious disease-control policy-making via perfor… (see more)ming inference in existing epidemiological dynamics models. The kind of inference tasks undertaken include computing, for planning purposes, the posterior distribution over putatively controllable, via direct policy-making choices, simulation model parameters that give rise to acceptable disease progression outcomes. Neither the full capabilities of such inference automation software tools nor their utility for planning is widely disseminated at the current time. Timely gains in understanding about these tools and how they can be used may lead to more fine-grained and less economically damaging policy prescriptions, particularly during the current COVID-19 pandemic.
2020-03-30
arXiv.org (preprint)
dblp.uni-trier.de