Publications

Global Surveillance of COVID-19 by mining news media using a multi-source dynamic embedded topic model.
Yue Li
Pratheeksha Nair
Zhi Wen
Imane Chafi
Anya Okhmatovskaia
Guido Powell
Yannan Shen
David L. Buckeridge
As the COVID-19 pandemic continues to unfold, understanding the global impact of non-pharmacological interventions (NPI) is important for fo… (voir plus)rmulating effective intervention strategies, particularly as many countries prepare for future waves. We used a machine learning approach to distill latent topics related to NPI from large-scale international news media. We hypothesize that these topics are informative about the timing and nature of implemented NPI, dependent on the source of the information (e.g., local news versus official government announcements) and the target countries. Given a set of latent topics associated with NPI (e.g., self-quarantine, social distancing, online education, etc), we assume that countries and media sources have different prior distributions over these topics, which are sampled to generate the news articles. To model the source-specific topic priors, we developed a semi-supervised, multi-source, dynamic, embedded topic model. Our model is able to simultaneously infer latent topics and learn a linear classifier to predict NPI labels using the topic mixtures as input for each news article. To learn these models, we developed an efficient end-to-end amortized variational inference algorithm. We applied our models to news data collected and labelled by the World Health Organization (WHO) and the Global Public Health Intelligence Network (GPHIN). Through comprehensive experiments, we observed superior topic quality and intervention prediction accuracy, compared to the baseline embedded topic models, which ignore information on media source and intervention labels. The inferred latent topics reveal distinct policies and media framing in different countries and media sources, and also characterize reaction to COVID-19 and NPI in a semantically meaningful manner. Our PyTorch code is available on Github (htps://github.com/li-lab-mcgill/covid19_media).
2020-11-09
Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (publié)
doi.org
On Posterior Collapse and Encoder Feature Dispersion in Sequence VAEs.
Teng Long
Yanshuai Cao
Jackie CK Cheung
Variational autoencoders (VAEs) hold great potential for modelling text, as they could in theory separate high-level semantic and syntactic … (voir plus)properties from local regularities of natural language. Practically, however, VAEs with autoregressive decoders often suffer from posterior collapse, a phenomenon where the model learns to ignore the latent variables, causing the sequence VAE to degenerate into a language model. In this paper, we argue that posterior collapse is in part caused by the lack of dispersion in encoder features. We provide empirical evidence to verify this hypothesis, and propose a straightforward fix using pooling. This simple technique effectively prevents posterior collapse, allowing model to achieve significantly better data log-likelihood than standard sequence VAEs. Comparing to existing work, our proposed method is able to achieve comparable or superior performances while being more computationally efficient.
2020-11-09
(publié)
www.semanticscholar.org
Approximate Planning and Learning for Partially Observed Systems
Aditya Mahajan
2020-11-08
International Conference of Control, Dynamic Systems, and Robotics (publié)
doi.org
DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning
Timo Milbich
Karsten Roth
Homanga Bharadhwaj
Samarth Sinha
Yoshua Bengio
Björn Ommer
Joseph Paul Cohen
2020-11-06
Computer Vision – ECCV 2020 (publié)
doi.org
arxiv.org
Effectiveness of quarantine and testing to prevent COVID-19 transmission from arriving travelers
Russell Wa
David L Buckeridge
2020-11-03
medRxiv (prépublication)
doi.org
Explainability and Interpretability: Keys to Deep Medicine
Arash Shaban-Nejad
Martin Michalowski
David L Buckeridge
2020-11-02
Explainable AI in Healthcare and Medicine (publié)
doi.org
A Study of Policy Gradient on a Class of Exactly Solvable Models
Gavin McCracken
Colin Daniels
Rosie Zhao
Anna M. Brandenberger
Prakash Panangaden
Doina Precup
Policy gradient methods are extensively used in reinforcement learning as a way to optimize expected return. In this paper, we explore the e… (voir plus)volution of the policy parameters, for a special class of exactly solvable POMDPs, as a continuous-state Markov chain, whose transition probabilities are determined by the gradient of the distribution of the policy's value. Our approach relies heavily on random walk theory, specifically on affine Weyl groups. We construct a class of novel partially observable environments with controllable exploration difficulty, in which the value distribution, and hence the policy parameter evolution, can be derived analytically. Using these environments, we analyze the probabilistic convergence of policy gradient to different local maxima of the value function. To our knowledge, this is the first approach developed to analytically compute the landscape of policy gradient in POMDPs for a class of such environments, leading to interesting insights into the difficulty of this problem.
2020-11-02
ArXiv (prépublication)
arxiv.org
arxiv.org
Urban Night Scenery Reconstruction by Day-night Registration and Synthesis
Andi Dai
David Meger
2020-11-02
SIGSPATIAL/GIS (publié)
doi.org
ComplexDataLab at WNUT-2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents
Kellin Pelrine
Jacob Danovitch
Albert Orozco Camacho
Reihaneh Rabbany
Given the global scale of COVID-19 and the flood of social media content related to it, how can we find informative discussions? We present … (voir plus)Gapformer, which effectively classifies content as informative or not. It reformulates the problem as graph classification, drawing on not only the tweet but connected webpages and entities. We leverage a pre-trained language model as well as the connections between nodes to learn a pooled representation for each document network. We show it outperforms several competitive baselines and present ablation studies supporting the benefit of the linked information. Code is available on Github.
2020-10-31
WNUT (publié)
doi.org
Deconstructing word embedding algorithms
Kian Kenyon-Dean
Edward Newell
Jackie Chi Kit Cheung
Word embeddings are reliable feature representations of words used to obtain high quality results for various NLP applications. Uncontextual… (voir plus)ized word embeddings are used in many NLP tasks today, especially in resource-limited settings where high memory capacity and GPUs are not available. Given the historical success of word embeddings in NLP, we propose a retrospective on some of the most well-known word embedding algorithms. In this work, we deconstruct Word2vec, GloVe, and others, into a common form, unveiling some of the common conditions that seem to be required for making performant word embeddings. We believe that the theoretical findings in this paper can provide a basis for more informed development of future models.
2020-10-31
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (publié)
doi.org
arxiv.org
Diversity-Enriched Option-Critic
Anand Kamat
Doina Precup
Temporal abstraction allows reinforcement learning agents to represent knowledge and develop strategies over different temporal scales. The … (voir plus)option-critic framework has been demonstrated to learn temporally extended actions, represented as options, end-to-end in a model-free setting. However, feasibility of option-critic remains limited due to two major challenges, multiple options adopting very similar behavior, or a shrinking set of task relevant options. These occurrences not only void the need for temporal abstraction, they also affect performance. In this paper, we tackle these problems by learning a diverse set of options. We introduce an information-theoretic intrinsic reward, which augments the task reward, as well as a novel termination objective, in order to encourage behavioral diversity in the option set. We show empirically that our proposed method is capable of learning options end-to-end on several discrete and continuous control tasks, outperforms option-critic by a wide margin. Furthermore, we show that our approach sustainably generates robust, reusable, reliable and interpretable options, in contrast to option-critic.
2020-10-31
arXiv (publié)
doi.org
arxiv.org
Experience Grounds Language
Yonatan Bisk
Ari Holtzman
Jesse D. Thomason
Jacob Andreas
Yoshua Bengio
Joyce Yue Chai
Mirella Lapata
Angeliki Lazaridou
Jonathan May
Aleksandr Nisnevich
Nicolas Pinto
Joseph Turian
2020-10-31
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (publié)
doi.org
arxiv.org