Publications

Traceability Network Analysis: A Case Study of Links in Issue Tracking Systems
Alexander Nicholson
Deeksha M. Arya
Jin L.C. Guo
Traceability links between software artifacts serve as an invaluable resource for reasoning about software products and their development pr… (voir plus)ocess. Most conventional methods for capturing traceability are based on pair-wise artifact relations such as trace matrices or navigable links between two directly related artifacts. However, this limited view of trace links ignores the propagating effect of artifact connections as well as the trace link properties at a project level. In this work, we propose the use of network structures to provide another perspective from which reasoning on a collective of trace events is possible. We explore various network analysis techniques in the issue tracking system of sixty-six open source projects. Our observation reveals two salient properties of the traceability network, i.e. scale free and triadic closure. These properties provide a strong indication of the applicability of network analysis tools and can be used to identify and examine important "hub" issues. As a stepping stone, these properties can further support project status analysis and link type prediction. As a proof-of-concept, we demonstrate the effectiveness of applying the triadic closure property to link type prediction.
2020-08-31
International Workshop on Artificial Intelligence for Requirements Engineering (publié)
doi.org
Learning to Drive Off Road on Smooth Terrain in Unstructured Environments Using an On-Board Camera and Sparse Aerial Images
Travis Manderson
Stefan Wapnick
David Meger
Gregory Dudek
We present a method for learning to drive on smooth terrain while simultaneously avoiding collisions in challenging off-road and unstructure… (voir plus)d outdoor environments using only visual inputs. Our approach applies a hybrid model-based and model-free reinforcement learning method that is entirely self-supervised in labeling terrain roughness and collisions using on-board sensors. Notably, we provide both first-person and overhead aerial image inputs to our model. We find that the fusion of these complementary inputs improves planning foresight and makes the model robust to visual obstructions. Our results show the ability to generalize to environments with plentiful vegetation, various types of rock, and sandy trails. During evaluation, our policy attained 90% smooth terrain traversal and reduced the proportion of rough terrain driven over by 6.1 times compared to a model using only first-person imagery.
2020-08-30
2020 IEEE International Conference on Robotics and Automation (ICRA) (publié)
doi.org
arxiv.org
A Neural Network Based Approach to Domain Modelling Relationships and Patterns Recognition
Rijul Saini
Gunter Mussbacher
Jin L.C. Guo
Jörg Kienzle
Model-Driven Software Engineering advocates the use of models and their transformations across different stages of software engineering to b… (voir plus)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.
2020-08-30
2020 IEEE Tenth International Model-Driven Requirements Engineering (MoDRE) (publié)
doi.org
Information correspondence between types of documentation for APIs
Deeksha M. Arya
Jin L.C. Guo
Martin P. Robillard
2020-08-25
Empirical Software Engineering (publié)
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 Kording
Blake Richards
Danilo Bzdok
Recently, deep learning has unlocked unprecedented success in various domains, especially using images, text, and speech. However, deep lear… (voir plus)ning is only beneficial if the data have nonlinear relationships and if they are exploitable at available sample sizes. We systematically profiled the performance of deep, kernel, and linear models as a function of sample size on UKBiobank brain images against established machine learning references. On MNIST and Zalando Fashion, prediction accuracy consistently improves when escalating from linear models to shallow-nonlinear models, and further improves with deep-nonlinear models. In contrast, using structural or functional brain scans, simple linear models perform on par with more complex, highly parameterized models in age/sex prediction across increasing sample sizes. In sum, linear models keep improving as the sample size approaches ~10,000 subjects. Yet, nonlinearities for predicting common phenotypes from typical brain scans remain largely inaccessible to the examined kernel and deep learning methods.
2020-08-24
Nature Communications (publié)
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
2020-08-20
Medical Image Analysis (publié)
doi.org
arxiv.org
Laplacian Change Point Detection for Dynamic Graphs
Shenyang Huang
Yasmeen Hitti
Guillaume Rabusseau
Reihaneh Rabbany
2020-08-19
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (publié)
doi.org
arxiv.org
Fast reinforcement learning with generalized policy updates
Andre Barreto
Shaobo Hou
Diana Borsa
David Silver
Doina Precup
The combination of reinforcement learning with deep learning is a promising approach to tackle important sequential decision-making problems… (voir plus) that are currently intractable. One obstacle to overcome is the amount of data needed by learning systems of this type. In this article, we propose to address this issue through a divide-and-conquer approach. We argue that complex decision problems can be naturally decomposed into multiple tasks that unfold in sequence or in parallel. By associating each task with a reward function, this problem decomposition can be seamlessly accommodated within the standard reinforcement-learning formalism. The specific way we do so is through a generalization of two fundamental operations in reinforcement learning: policy improvement and policy evaluation. The generalized version of these operations allow one to leverage the solution of some tasks to speed up the solution of others. If the reward function of a task can be well approximated as a linear combination of the reward functions of tasks previously solved, we can reduce a reinforcement-learning problem to a simpler linear regression. When this is not the case, the agent can still exploit the task solutions by using them to interact with and learn about the environment. Both strategies considerably reduce the amount of data needed to solve a reinforcement-learning problem.
2020-08-16
Proceedings of the National Academy of Sciences of the United States of America (publié)
doi.org
Mastering Rate based Curriculum Learning
Lucas Willems
Salem Lahlou
Yoshua Bengio
2020-08-13
ArXiv (prépublication)
arxiv.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… (voir plus)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].
2020-08-11
ArXiv (prépublication)
openreview.net
openreview.net
Prediction, Not Association, Paves the Road to Precision Medicine
Danilo Bzdok
Gael Varoquaux
Ewout W. Steyerberg
2020-08-11
JAMA Psychiatry (publié)
doi.org
Robust motion in-betweening
Félix Harvey
Mike Yurick
D. Nowrouzezahrai
Christopher Pal
In this work we present a novel, robust transition generation technique that can serve as a new tool for 3D animators, based on adversarial … (voir plus)recurrent neural networks. The system synthesises high-quality motions that use temporally-sparse keyframes as animation constraints. This is reminiscent of the job of in-betweening in traditional animation pipelines, in which an animator draws motion frames between provided keyframes. We first show that a state-of-the-art motion prediction model cannot be easily converted into a robust transition generator when only adding conditioning information about future keyframes. To solve this problem, we then propose two novel additive embedding modifiers that are applied at each timestep to latent representations encoded inside the network's architecture. One modifier is a time-to-arrival embedding that allows variations of the transition length with a single model. The other is a scheduled target noise vector that allows the system to be robust to target distortions and to sample different transitions given fixed keyframes. To qualitatively evaluate our method, we present a custom MotionBuilder plugin that uses our trained model to perform in-betweening in production scenarios. To quantitatively evaluate performance on transitions and generalizations to longer time horizons, we present well-defined in-betweening benchmarks on a subset of the widely used Human3.6M dataset and on LaFAN1, a novel high quality motion capture dataset that is more appropriate for transition generation. We are releasing this new dataset along with this work, with accompanying code for reproducing our baseline results.
2020-08-11
ACM Transactions on Graphics (publié)
doi.org
arxiv.org