Publications

To Write Code: The Cultural Fabrication of Programming Notation and Practice
Ian Arawjo
2020-04-20
International Conference on Human Factors in Computing Systems (publié)
doi.org
Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus
Bang Liu
Haojie Wei
Di Niu
Haolan Chen
Yancheng He
The ability to ask questions is important in both human and machine intelligence. Learning to ask questions helps knowledge acquisition, imp… (voir plus)roves question-answering and machine reading comprehension tasks, and helps a chatbot to keep the conversation flowing with a human. Existing question generation models are ineffective at generating a large amount of high-quality question-answer pairs from unstructured text, since given an answer and an input passage, question generation is inherently a one-to-many mapping. In this paper, we propose Answer-Clue-Style-aware Question Generation (ACS-QG), which aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale by imitating the way a human asks questions. Our system consists of: i) an information extractor, which samples from the text multiple types of assistive information to guide question generation; ii) neural question generators, which generate diverse and controllable questions, leveraging the extracted assistive information; and iii) a neural quality controller, which removes low-quality generated data based on text entailment. We compare our question generation models with existing approaches and resort to voluntary human evaluation to assess the quality of the generated question-answer pairs. The evaluation results suggest that our system dramatically outperforms state-of-the-art neural question generation models in terms of the generation quality, while being scalable in the meantime. With models trained on a relatively smaller amount of data, we can generate 2.8 million quality-assured question-answer pairs from a million sentences found in Wikipedia.
2020-04-19
Proceedings of The Web Conference 2020 (publié)
doi.org
arxiv.org
A Unifying Framework for Fairness-Aware Influence Maximization
Golnoosh Farnadi
Behrouz Babaki
Michel Gendreau
The problem of selecting a subset of nodes with greatest influence in a graph, commonly known as influence maximization, has been well studi… (voir plus)ed over the past decade. This problem has real world applications which can potentially affect lives of individuals. Algorithmic decision making in such domains raises concerns about their societal implications. One of these concerns, which surprisingly has only received limited attention so far, is algorithmic bias and fairness. We propose a flexible framework that extends and unifies the existing works in fairness-aware influence maximization. This framework is based on an integer programming formulation of the influence maximization problem. The fairness requirements are enforced by adding linear constraints or modifying the objective function. Contrary to the previous work which designs specific algorithms for each variant, we develop a formalism which is general enough for specifying different notions of fairness. A problem defined in this formalism can be then solved using efficient mixed integer programming solvers. The experimental evaluation indicates that our framework not only is general but also is competitive with existing algorithms.
2020-04-19
Companion Proceedings of the Web Conference 2020 (publié)
doi.org
Role-Wise Data Augmentation for Knowledge Distillation
Jie Fu
Xue Geng
Zhijian Duan
Bohan Zhuang
Xingdi Yuan
Adam Trischler
Jie Lin
Vijay Chandrasekhar
Christopher Pal
Hao Dong
2020-04-18
ArXiv (prépublication)
openreview.net
openreview.net
Neuropsychiatric copy number variants exert shared effects on human brain structure
Claudia Modenato
Kumar Kuldeep
Clara A. Moreau
Martin-Brevet Sandra
Huguet Guillaume
Schramm Catherine
Younis Nadine
Martin Charles-Olivier
C.O. Martin
Martineau Jean-Louis
Petra Tamer
Lippé Sarah
Thébault-Dagher Fanny
Valérie Côté
Charlebois A.R.
Deguire F.
Maillard Anne M.
Rodriguez-Herreros Borja
Pain Aurèlie
Richetin Sonia … (voir 15 de plus)
16p11.2 European Consortium
Simons Variation in Individuals Project (VIP) Consortium
Kushan Leila
Silva Ana I.
Melie-Garcia Lester
Marianne B.M. van den Bree
Douard Elise
M. J. Owen
Hall Jeremy
Douard Elise
Chakravarty Mallar
Danilo Bzdok
Carrie E. Bearden
Draganski Bogdan
Sébastien Jacquemont
Neuropsychiatric CNVs share neuroanatomical signatures characterized by a parsimonious set of brain dimensions. The latter may underlie the … (voir plus)risk conferred by CNVs for a similar spectrum of neuropsychiatric conditions.
2020-04-16
medRxiv (prépublication)
doi.org
Uncertainty Evaluation Metric for Brain Tumour Segmentation
Raghav Mehta
Angelos Filos
Yarin Gal
Tal Arbel
In this paper, we develop a metric designed to assess and rank uncertainty measures for the task of brain tumour sub-tissue segmentation in … (voir plus)the BraTS 2019 sub-challenge on uncertainty quantification. The metric is designed to: (1) reward uncertainty measures where high confidence is assigned to correct assertions, and where incorrect assertions are assigned low confidence and (2) penalize measures that have higher percentages of under-confident correct assertions. Here, the workings of the components of the metric are explored based on a number of popular uncertainty measures evaluated on the BraTS 2019 dataset.
2020-04-16
MIDL.io/2020/Conference (publié)
openreview.net
openreview.net
Systems consolidation impairs behavioral flexibility
Sankirthana Sathiyakumar
Sofia Skromne Carrasco
Lydia Saad
Blake Aaron Richards
2020-04-14
Learning & memory (Cold Spring Harbor, N.Y.) (publié)
doi.org
On generalized surrogate duality in mixed-integer nonlinear programming
Benjamin Müller
Gonzalo Muñoz
Maxime Gasse
Ambros Gleixner
Andrea Lodi
Felipe Serrano
The most important ingredient for solving mixed-integer nonlinear programs (MINLPs) to global epsilon-optimality with spatial branch and bou… (voir plus)nd is a tight, computationally tractable relaxation. Due to both theoretical and practical considerations, relaxations of MINLPs are usually required to be convex. Nonetheless, current optimization solver can often successfully handle a moderate presence of nonconvexities, which opens the door for the use of potentially tighter nonconvex relaxations. In this work, we exploit this fact and make use of a nonconvex relaxation obtained via aggregation of constraints: a surrogate relaxation. These relaxations were actively studied for linear integer programs in the 70s and 80s, but they have been scarcely considered since. We revisit these relaxations in an MINLP setting and show the computational benefits and challenges they can have. Additionally, we study a generalization of such relaxation that allows for multiple aggregations simultaneously and present the first algorithm that is capable of computing the best set of aggregations. We propose a multitude of computational enhancements for improving its practical performance and evaluate the algorithm's ability to generate strong dual bounds through extensive computational experiments.
2020-04-13
Integer Programming and Combinatorial Optimization (publié)
doi.org
arxiv.org
Clustering for Continuous-Time Hidden Markov Models.
Yu Luo
David A. Stephens
David L Buckeridge
We develop clustering procedures for longitudinal trajectories based on a continuous-time hidden Markov model (CTHMM) and a generalized line… (voir plus)ar observation model. Specifically in this paper, we carry out infinite mixture model-based clustering for CTHMM and achieve inference using Markov chain Monte Carlo (MCMC). Specifically, for Bayesian nonparametric inference using a Dirichlet process mixture model, we utilize restricted Gibbs sampling split-merge proposals to expedite the MCMC algorithm. We employ the proposed algorithm to the simulated data as well as a large real data example, and the results demonstrate the desired performance of the new sampler.
2020-04-12
(publié)
www.semanticscholar.org
Establishing an evaluation metric to quantify climate change image realism
Sharon Zhou
Alexandra Luccioni
Gautier Cosne
Michael S. Bernstein
Yoshua Bengio
2020-04-06
Machine Learning: Science and Technology (publié)
doi.org
arxiv.org
Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction
Vishal Jain
William Fedus
Hugo Larochelle
Doina Precup
Bellemare Marc-Emmanuel
Text-based games are a natural challenge domain for deep reinforcement learning algorithms. Their state and action spaces are combinatoriall… (voir plus)y large, their reward function is sparse, and they are partially observable: the agent is informed of the consequences of its actions through textual feedback. In this paper we emphasize this latter point and consider the design of a deep reinforcement learning agent that can play from feedback alone. Our design recognizes and takes advantage of the structural characteristics of text-based games. We first propose a contextualisation mechanism, based on accumulated reward, which simplifies the learning problem and mitigates partial observability. We then study different methods that rely on the notion that most actions are ineffectual in any given situation, following Zahavy et al.'s idea of an admissible action. We evaluate these techniques in a series of text-based games of increasing difficulty based on the TextWorld framework, as well as the iconic game Zork. Empirically, we find that these techniques improve the performance of a baseline deep reinforcement learning agent applied to text-based games.
2020-04-02
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
CNN Detection of New and Enlarging Multiple Sclerosis Lesions from Longitudinal Mri Using Subtraction Images
Nazanin Mohammadi Sepahvand
Douglas Arnold
Tal Arbel
Accurate detection and segmentation of new lesional activity in longitudinal Magnetic Resonance Images (MRIs) of patients with Multiple Scle… (voir plus)rosis (MS) is important for monitoring disease activity, as well as for assessing treatment effects. In this work, we present the first deep learning framework to automatically detect and segment new and enlarging (NE) T2w lesions from longitudinal brain MRIs acquired from relapsing-remitting MS (RRMS) patients. The proposed framework is an adapted 3D U-Net [1] which includes as inputs the reference multi-modal MRI and T2-weighted lesion maps, as well an attention mechanism based on the subtraction MRI (between the two timepoints) which serves to assist the network in learning to differentiate between real anatomical change and artifactual change, while constraining the search space for small lesions. Experiments on a large, proprietary, multi -center, multi-modal, clinical trial dataset consisting of 1677 multi-modal scans illustrate that network achieves high overall detection accuracy (detection AUC=.95), outperforming (1) a U-Net without an attention mechanism (de-tection AUC=.93), (2) a framework based on subtracting independent T2-weighted segmentations (detection AUC=.57), and (3) DeepMedic (detection AUC=.84) [2], particularly for small lesions. In addition, the method was able to accurately classify patients as active/inactive with (sensitivities of. 69 and specificities of. 97).
2020-04-02
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) (publié)
doi.org