Publications

TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion

Jiapeng Wu

Meng Cao

Jackie Chi Kit Cheung

William L. Hamilton

Inferring missing facts in temporal knowledge graphs (TKGs) is a fundamental and challenging task. Previous works have approached this probl… (voir plus)em by augmenting methods for static knowledge graphs to leverage time-dependent representations. However, these methods do not explicitly leverage multi-hop structural information and temporal facts from recent time steps to enhance their predictions. Additionally, prior work does not explicitly address the temporal sparsity and variability of entity distributions in TKGs. We propose the Temporal Message Passing (TeMP) framework to address these challenges by combining graph neural networks, temporal dynamics models, data imputation and frequency-based gating techniques. Experiments on standard TKG tasks show that our approach provides substantial gains compared to the previous state of the art, achieving a 10.7% average relative improvement in Hits@10 across three standard benchmarks. Our analysis also reveals important sources of variability both within and across TKG datasets, and we introduce several simple but strong baselines that outperform the prior state of the art in certain settings.

2020-10-31

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (publié)

doi.org

arxiv.org

TESA: A Task in Entity Semantic Aggregation for Abstractive Summarization

Clement Jumel

Annie Priyadarshini Louis

Jackie CK Cheung

Human-written texts contain frequent generalizations and semantic aggregation of content. In a document, they may refer to a pair of named e… (voir plus)ntities such as ‘London’ and ‘Paris’ with different expressions: “the major cities”, “the capital cities” and “two European cities”. Yet generation, especially, abstractive summarization systems have so far focused heavily on paraphrasing and simplifying the source content, to the exclusion of such semantic abstraction capabilities. In this paper, we present a new dataset and task aimed at the semantic aggregation of entities. TESA contains a dataset of 5.3K crowd-sourced entity aggregations of Person, Organization, and Location named entities. The aggregations are document-appropriate, meaning that they are produced by annotators to match the situational context of a given news article from the New York Times. We then build baseline models for generating aggregations given a tuple of entities and document context. We finetune on TESA an encoder-decoder language model and compare it with simpler classification methods based on linguistically informed features. Our quantitative and qualitative evaluations show reasonable performance in making a choice from a given list of expressions, but free-form expressions are understandably harder to generate and evaluate.

2020-10-31

Conference on Empirical Methods in Natural Language Processing (publié)

doi.org

Neuroimaging: into the Multiverse

Jessica Dafflon

Pedro F. da Costa

František Váša

Ricardo Pio Monti

Danilo Bzdok

Peter J. Hellyer

Federico Turkheimer

Jonathan Smallwood

Emily J. H. Jones

Robert Leech

For most neuroimaging questions the huge range of possible analytic choices leads to the possibility that conclusions from any single analyt… (voir plus)ic approach may be misleading. Examples of possible choices include the motion regression approach used and smoothing and threshold factors applied during the processing pipeline. Although it is possible to perform a multiverse analysis that evaluates all possible analytic choices, this can be computationally challenging and repeated sequential analyses on the same data can compromise inferential and predictive power. Here, we establish how active learning on a low-dimensional space that captures the inter-relationships between analysis approaches can be used to efficiently approximate the whole multiverse of analyses. This approach balances the benefits of a multiverse analysis without the accompanying cost to statistical power, computational power and the integrity of inferences. We illustrate this approach with a functional MRI dataset of functional connectivity across adolescence, demonstrating how a multiverse of graph theoretic and simple pre-processing steps can be efficiently navigated using active learning. Our study shows how this approach can identify the subset of analysis techniques (i.e., pipelines) which are best able to predict participants’ ages, as well as allowing the performance of different approaches to be quantified.

2020-10-28

bioRxiv (prépublication)

doi.org

The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach

Iulian Vlad Serban

Chinnadhurai Sankar

Michael Pieper

Joelle Pineau

Yoshua Bengio

Deep reinforcement learning has recently shown many impressive successes. However, one major obstacle towards applying such methods to real-… (voir plus)world problems is their lack of data-efficiency. To this end, we propose the Bottleneck Simulator: a model-based reinforcement learning method which combines a learned, factorized transition model of the environment with rollout simulations to learn an effective policy from few examples. The learned transition model employs an abstract, discrete (bottleneck) state, which increases sample efficiency by reducing the number of model parameters and by exploiting structural properties of the environment. We provide a mathematical analysis of the Bottleneck Simulator in terms of fixed points of the learned policy, which reveals how performance is affected by four distinct sources of error: an error related to the abstract space structure, an error related to the transition model estimation variance, an error related to the transition model estimation bias, and an error related to the transition model class bias. Finally, we evaluate the Bottleneck Simulator on two natural language processing tasks: a text adventure game and a real-world, complex dialogue response selection task. On both tasks, the Bottleneck Simulator yields excellent performance beating competing approaches.

2020-10-26

Journal of Artificial Intelligence Research (publié)

doi.org

arxiv.org

Association between extreme precipitation, drinking water and acute gastrointestinal illness in the Great Lakes

R. Graydon

M. Mezzacapo

J. Boehme

David L Buckeridge

S. Foldy

T. Edge

J. Brubacher

L. Chan

M. Dellinger

E. Faustman

J. Rose

T. Takaro

2020-10-25

ISEE Conference Abstracts (publié)

doi.org

DoMoBOT: a bot for automated and interactive domain modelling

Rijul Saini

Gunter Mussbacher

Jin L.C. Guo

Jörg Kienzle

Domain modelling transforms domain problem descriptions written in natural language (NL) into analyzable and concise domain models (class di… (voir plus)agrams) during requirements analysis or the early stages of design in software development. Since the practice of domain modelling requires time in addition to modelling skills and experience, several approaches have been proposed to automate or semi-automate the construction of domain models from problem descriptions expressed in NL. Despite the existing work on domain model extraction, some significant challenges remain unaddressed: (i) the extracted domain models are not accurate enough to be used directly or with minor modifications in software development, (ii) existing approaches do not facilitate the tracing of the rationale behind the modelling decisions taken by the model extractor, and (iii) existing approaches do not provide interactive interfaces to update the extracted domain models. Therefore, in this paper, we introduce a domain modelling bot called DoMoBOT, explain its architecture, and implement it in the form of a web-based prototype tool. The bot automatically extracts a domain model from a problem description written in NL with an accuracy higher than existing approaches. Furthermore, the bot enables modellers to update a part of the extracted domain model and in response the bot re-configures the other parts of the domain model pro-actively. To improve the accuracy of extracted domain models, we combine the techniques of Natural Language Processing and Machine Learning. Finally, we evaluate the accuracy of the extracted domain models.

2020-10-25

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings (publié)

doi.org

Learning Domain Randomization Distributions for Training Robust Locomotion Policies

Melissa Mozian

Juan Camilo Gamboa Higuera

David Meger

Gregory Dudek

This paper considers the problem of learning behaviors in simulation without knowledge of the precise dynamical properties of the target rob… (voir plus)ot platform(s). In this context, our learning goal is to mutually maximize task efficacy on each environment considered and generalization across the widest possible range of environmental conditions. The physical parameters of the simulator are modified by a component of our technique that learns the Domain Randomization (DR) that is appropriate at each learning epoch to maximally challenge the current behavior policy, without being overly challenging, which can hinder learning progress. This so-called sweet spot distribution is a selection of simulated domains with the following properties: 1) The trained policy should be successful in environments sampled from the domain randomization distribution; and 2) The DR distribution made as wide as possible, to increase variability in the environments. These properties aim to ensure the trajectories encountered in the target system are close to those observed during training, as existing methods in machine learning are better suited for interpolation than extrapolation. We show how adapting the DR distribution while training context-conditioned policies results in improvements on jump-start and asymptotic performance when transferring a learned policy to the target environment1.

2020-10-23

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publié)

doi.org

PresSense: Passive Respiration Sensing via Ambient WiFi Signals in Noisy Environments

Yi Tian Xu

X. T. Chen

Xue Liu

David Meger

Gregory Dudek

Passive sensing with ambient WiFi signals is a promising technique that will enable new types of human-robot interactions while preserving u… (voir plus)sers' privacy. Here, we present PresSense, a system for human respiration sensing in noisy environments. Unlike existing WiFi-based respiration sensors, we employ a human presence detector, improving the robustness in scenarios where no human is present in an Area Of Interest (AOI). We also integrate our novel feature, Peak Distance Histogram (PDH), with other classic WiFi features to achieve better accuracy when someone is present in the AOI. We tested our system using commodity WiFi devices in an office room. Our PresSense outperforms the state of the arts in both respiration rate estimation and presence detection.

2020-10-23

IEEE/RJS International Conference on Intelligent RObots and Systems (publié)

doi.org

Veille sur les outils numériques en santé dans le contexte de COVID-19

Aude Motulsky

Philippe Després

Cecile Petitgand

Jean Noel Nikiema

Catherine Régis

Jean-Louis Denis

2020-10-22

(publié)

doi.org

NU-GAN: High resolution neural upsampling with GAN

In this paper, we propose NU-GAN, a new method for resampling audio from lower to higher sampling rates (upsampling). Audio upsampling is an… (voir plus) important problem since productionizing generative speech technology requires operating at high sampling rates. Such applications use audio at a resolution of 44.1 kHz or 48 kHz, whereas current speech synthesis methods are equipped to handle a maximum of 24 kHz resolution. NU-GAN takes a leap towards solving audio upsampling as a separate component in the text-to-speech (TTS) pipeline by leveraging techniques for audio generation using GANs. ABX preference tests indicate that our NU-GAN resampler is capable of resampling 22 kHz to 44.1 kHz audio that is distinguishable from original audio only 7.4% higher than random chance for single speaker dataset, and 10.8% higher than chance for multi-speaker dataset.

2020-10-21

ArXiv (prépublication)

arxiv.org

Explicitly Modeling Syntax in Language Model improves Generalization

Syntax is fundamental to our thinking about language. Although neural networks are very successful in many tasks, they do not explicitly mod… (voir plus)el syntactic structure. Failing to capture the structure of inputs could lead to generalization problems and over-parametrization. In the present work, we propose a new syntax-aware language model: Syntactic Ordered Memory (SOM). The model explicitly models the structure with a one-step look-ahead parser and maintains the conditional probability setting of the standard language model. Experiments show that SOM can achieve strong results in language modeling and syntactic generalization tests, while using fewer parameters then other models.

2020-10-20

arXiv.org (prépublication)

dblp.uni-trier.de

Cross-Modal Information Maximization for Medical Imaging: CMIM

Tristan Sylvain

Francis Dutil

Tess Berthier

Lisa Di Jorio

Margaux Luck

R Devon Hjelm

Yoshua Bengio

2020-10-19