Publications

A Neural Network Based Approach to Domain Modelling Relationships and Patterns Recognition
Rijul Saini
Gunter Mussbacher
Jin Guo
Jörg Kienzle
Model-Driven Software Engineering advocates the use of models and their transformations across different stages of software engineering to b… (see more)etter understand and analyze systems under development. Domain modelling is used during requirements analysis or the early stages of design to transform informal requirements written in natural language to domain models which are analyzable and more concise. Since domain modelling is time-consuming and requires modelling skills and experience, many approaches have been proposed to extract domain concepts and relationships automatically using extraction rules. However, relationships and patterns are often hidden in the sentences of a problem description. Automatic recognition of relationships or patterns in those cases requires context information and external knowledge of participating domain concepts, which goes beyond what is possible with extraction rules. In this paper, we draw on recent work on domain model extraction and envision a novel technique where sentence boundaries are customized and clusters of sentences are created for domain concepts. The technique further exploits a BiLSTM neural network model to identify relationships and patterns among domain concepts. We also present a classification strategy for relationships and patterns and use it to instantiate our technique. Preliminary results indicate that this novel idea is promising and warrants further research.
2020-08-31
2020 IEEE Tenth International Model-Driven Requirements Engineering (MoDRE) (published)
doi.org
Information correspondence between types of documentation for APIs
Deeksha M. Arya
Jin Guo
Martin P. Robillard
2020-08-26
Empirical Software Engineering (published)
doi.org
Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets
Marc-Andre Schulz
B.T. Thomas Yeo
Joshua T. Vogelstein
Janaina Mourao-Miranada
Jakob N. Kather
Konrad Paul Kording
Blake Richards
Danilo Bzdok
2020-08-25
Nature Communications (published)
doi.org
BIAS: Transparent reporting of biomedical image analysis challenges
Lena Maier-Hein
Annika Reinke
Michal Kozubek
Anne L. Martel
Tal Arbel
Matthias Eisenmann
Allan Hanbury
Pierre Jannin
Henning Müller
Sinan Onogur
Julio Saez-Rodriguez
Bram van Ginneken
Annette Kopp-Schneider
Bennett Landman
2020-08-21
Medical Image Analysis (published)
doi.org
arxiv.org
Laplacian Change Point Detection for Dynamic Graphs
Shenyang Huang
Yasmeen Hitti
Guillaume Rabusseau
Reihaneh Rabbany
2020-08-20
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (published)
doi.org
arxiv.org
Adaptive Learning of Tensor Network Structures
Meraj Hashemizadeh
Michelle Liu
Jacob Miller
Guillaume Rabusseau
Tensor Networks (TN) offer a powerful framework to efficiently represent very high-dimensional objects. TN have recently shown their potenti… (see more)al for machine learning applications and offer a unifying view of common tensor decomposition models such as Tucker, tensor train (TT) and tensor ring (TR). However, identifying the best tensor network structure from data for a given task is challenging. In this work, we leverage the TN formalism to develop a generic and efficient adaptive algorithm to jointly learn the structure and the parameters of a TN from data. Our method is based on a simple greedy approach starting from a rank one tensor and successively identifying the most promising tensor network edges for small rank increments. Our algorithm can adaptively identify TN structures with small number of parameters that effectively optimize any differentiable objective function. Experiments on tensor decomposition, tensor completion and model compression tasks demonstrate the effectiveness of the proposed algorithm. In particular, our method outperforms the state-of-the-art evolutionary topology search [Li and Sun, 2020] for tensor decomposition of images (while being orders of magnitude faster) and finds efficient tensor network structures to compress neural networks outperforming popular TT based approaches [Novikov et al., 2015].
2020-08-12
ArXiv (preprint)
openreview.net
openreview.net
''COGITO in Space'': a thought experiment in exo-neurobiology
Daniela de Paulis
Stephen Whitmarsh
Robert Oostenveld
Guillaume Dumas
Michael Sanders
2020-08-04
(published)
doi.org
SeroTracker: a global SARS-CoV-2 seroprevalence dashboard
Rahul K. Arora
Abel Joseph
Jordan Van Wyk
Simona Rocco
Austin Atmaja
Ewan May
Tingting Yan
Niklas Bobrovitz
Jonathan Chevrier
Matthew P. Cheng
Tyler Williamson
David Buckeridge
2020-08-04
Lancet. Infectious Diseases (Print) (published)
doi.org
Implicit Regularization in Deep Learning: A View from Function Space
Aristide Baratin
Thomas George
César Laurent
(Rex) Devon Hjelm
Guillaume Lajoie
Pascal Vincent
Simon Lacoste-Julien
2020-08-03
ArXiv (preprint)
arxiv.org
arxiv.org
Implicit Regularization in Deep Learning: A View from Function Space
Aristide Baratin
Thomas George
César Laurent
(Rex) Devon Hjelm
Guillaume Lajoie
Pascal Vincent
Simon Lacoste-Julien
We approach the problem of implicit regularization in deep learning from a geometrical viewpoint. We highlight a possible regularization eff… (see more)ect induced by a dynamical alignment of the neural tangent features introduced by Jacot et al, along a small number of task-relevant directions. By extrapolating a new analysis of Rademacher complexity bounds in linear models, we propose and study a new heuristic complexity measure for neural networks which captures this phenomenon, in terms of sequences of tangent kernel classes along in the learning trajectories.
2020-08-03
ArXiv (preprint)
arxiv.org
arxiv.org
BDD-based optimization for the quadratic stable set problem
Jaime E. González
Andr'e Augusto Cire
Andrea Lodi
Louis-Martin Rousseau
2020-08-01
Discrete Optimization (published)
doi.org
BDD-based optimization for the quadratic stable set problem
Jaime E. González
Andr'e Augusto Cire
Andrea Lodi
Louis-Martin Rousseau
2020-08-01
Discrete Optimization (published)
doi.org