Publications

Generating GitHub Repository Descriptions: A Comparison of Manual and Automated Approaches
Jazlyn Hellman
Eunbee Jang
Christoph Treude
Chenzhun Huang
Jin L.C. Guo
Given the vast number of repositories hosted on GitHub, project discovery and retrieval have become increasingly important for GitHub users.… (voir plus) Repository descriptions serve as one of the first points of contact for users who are accessing a repository. However, repository owners often fail to provide a high-quality description; instead, they use vague terms, the purpose of the repository is poorly explained, or the description is omitted entirely. In this work, we examine the current practice of writing GitHub repository descriptions. Our investigation leads to the proposal of the LSP (Language, Software technology, and Purpose) template to formulate good descriptions for GitHub repositories that are clear, concise, and informative. To understand the extent to which current automated techniques can support generating repository descriptions, we compare the performance of state-of-the-art text summarization methods on this task. Finally, our user study with GitHub users reveals that automated summarization can adequately be used for default description generation for GitHub repositories, while the descriptions which follow the LSP template offer the most effective instrument for communicating with GitHub users.
2021-10-24
ArXiv (prépublication)
arxiv.org
arxiv.org
Multimodal Data Visualization and Denoising with Integrated Diffusion
Manik Kuchroo
Abhinav Godavarthi
Alexander Tong
Guy Wolf
Smita Krishnaswamy
We propose a method called integrated diffusion for combining multimodal datasets, or data gathered via several different measurements on th… (voir plus)e same system, to create a joint data diffusion operator. As real world data suffers from both local and global noise, we introduce mechanisms to optimally calculate a diffusion operator that reflects the combined information from both modalities. We show the utility of this joint operator in data denoising, visualization and clustering, performing better than other methods to integrate and analyze multimodal data. We apply our method to multi-omic data generated from blood cells, measuring both gene expression and chromatin accessibility. Our approach better visualizes the geometry of the joint data, captures known cross-modality associations and identifies known cellular populations. More generally, integrated diffusion is broadly applicable to multimodal datasets generated in many medical and biological systems.
2021-10-24
PubMed Central (accepté)
doi.org
arxiv.org
CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding
Suzanne Ackloo
Rima Al-awar
Rommie E. Amaro
Cheryl H. Arrowsmith
Hatylas Azevedo
Robert A. Batey
Yoshua Bengio
Ulrich A.K. Betz
Cristian G. Bologa
John D. Chodera
Wendy D. Cornell
Ian Dunham
Gerhard F. Ecker
Kristina Edfeldt
Aled M. Edwards
Michael K. Gilson
Claudia R. Gordijo
Gerhard Hessler
Alexander Hillisch
Anders Hogner … (voir 19 de plus)
John J. Irwin
Johanna M. Jansen
Daniel Kuhn
Andrew R. Leach
Alpha A. Lee
Uta Lessel
John Moult
Ingo Muegge
Tudor I. Oprea
Benjamin G. Perry
Patrick Riley
Kumar Singh Saikatendu
Vijayaratnam Santhakumar
Matthieu Schapira
Cora Scholten
Matthew H. Todd
Masoud Vedadi
Andrea Volkamer
Timothy M. Willson
Computational approaches in drug discovery and development hold great promise, with artificial intelligence methods undergoing widespread co… (voir plus)ntemporary use, but the experimental validation of these new approaches is frequently inadequate. We are initiating Critical Assessment of Computational Hit-finding Experiments (CACHE) as a public benchmarking project that aims to accelerate the development of small molecule hit-finding algorithms by competitive assessment. Compounds will be identified by participants using a wide range of computational methods for dozens of protein targets selected for different types of prediction scenarios, as well as for their potential biological or pharmaceutical relevance. Community-generated predictions will be tested centrally and rigorously in an experimental hub(s), and all data, including the chemical structures of experimentally tested compounds, will be made publicly available without restrictions. The ability of a range of computational approaches to find novel compounds will be evaluated, compared, and published. The overarching goal of CACHE is to accelerate the development of computational chemistry methods by providing rapid and unbiased feedback to those developing methods, with an ancillary and valuable benefit of identifying new compound-protein binding pairs for biologically interesting targets. The initiative builds on the power of crowd sourcing and expands the open science paradigm for drug discovery.
2021-10-21
Nature Reviews Chemistry (publié)
doi.org
Probabilistic fine-tuning of pruning masks and PAC-Bayes self-bounded learning
Soufiane Hayou
Bo He
Gintare Karolina Dziugaite
2021-10-21
ArXiv (prépublication)
arxiv.org
arxiv.org
Convergence Analysis and Implicit Regularization of Feedback Alignment for Deep Linear Networks
Manuela Girotti
Ioannis Mitliagkas
Gauthier Gidel
We theoretically analyze the Feedback Alignment (FA) algorithm, an efficient alternative to backpropagation for training neural networks. We… (voir plus) provide convergence guarantees with rates for deep linear networks for both continuous and discrete dynamics. Additionally, we study incremental learning phenomena for shallow linear networks. Interestingly, certain specific initializations imply that negligible components are learned before the principal ones, thus potentially negatively affecting the effectiveness of such a learning algorithm; a phenomenon we classify as implicit anti-regularization. We also provide initialization schemes where the components of the problem are approximately learned by decreasing order of importance, thus providing a form of implicit regularization.
2021-10-19
arXiv (Cornell University) (prépublication)
doi.org
arxiv.org
Evaluation of real-life use of Point-Of-Care Rapid Antigen TEsting for SARS-CoV-2 in schools (EPOCRATES)
Ana C. Blanchard
Marc Desforges
Annie-Claude Labbé
Cat Tuong Nguyen
Yves Petit
Dominic Besner
Kate Zinszer
Olivier Séguin
Zineb Laghdir
Kelsey Adams
Marie-Ève Benoit
Geneviève Leduc
Jean Longtin
Ioannis Ragoussis
David L. Buckeridge
Caroline Quach
We evaluated the use of rapid antigen detection tests (RADT) for the diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-… (voir plus)2) infection in school settings to determine RADT’s performance compared to PCR. In this real-world, prospective observational cohort study, high-school students and staff were recruited from two high-schools in Montreal (Canada) and followed from January 25 th to June 10 th , 2021. Twenty-five percent of asymptomatic participants were tested weekly by RADT (nasal) and PCR (gargle). Class contacts of cases were tested. Symptomatic participants were tested by RADT (nasal) and PCR (nasal and gargle). The number of cases and outbreaks were compared to other high schools in the same area. Overall, 2,099 students and 286 school staff members consented to participate. The overall RADT’s specificity varied from 99.8 to 100%, with a lower sensitivity, varying from 28.6% in asymptomatic to 83.3% in symptomatic participants. Secondary cases were identified in 10 of 35 classes. Returning students to school after a 7-day quarantine, with a negative PCR on D6-7 after exposure, did not lead to subsequent outbreaks. Of cases for whom the source was known, 37 of 57 (72.5%) were secondary to household transmission, 13 (25%) to intra-school transmission and one to community contacts between students in the same school. RADT did not perform well as a screening tool in asymptomatic individuals. Reinforcing policies for symptom screening when entering schools and testing symptomatic individuals with RADT on the spot may avoid subsequent significant exposures in class. Rapid antigen tests were compared to standard PCR to diagnose SARS-CoV-2 infections in high-school students. They performed better in symptomatic individuals. Rapid antigen detection tests (RADT) are often used to diagnose respiratory pathogens at the point-of-care. Their performance characteristics vary, but they usually have high specificity and moderate sensitivity compared with PCR. RADT sensitivity ranged from 28.6% in asymptomatic individuals to 83.3% in symptomatic individuals. Return to school after 7 days of quarantine was safe in exposed students. Secondary cases were identified in 28% of classes with an index case.
2021-10-13
medRxiv (prépublication)
doi.org
Reduced Axon Calibre in the Associative Striatum of the Sapap3 Knockout Mouse
Eliana Lopes Rio da Silva Lousada
Mathieu Boudreau
Julien Cohen‐Adad
Brahim Nait‐Oumesmar
Eric Burguière
Christiane Schreiweis
Pathological repetitive behaviors are a common feature of different neuropsychiatric disorders such as obsessive-compulsive disorder or Gill… (voir plus)es de la Tourette syndrome. The Sapap3 knockout mouse (Sapap3-KO) is the current reference model used in translational psychiatry to study co-morbid repetitive behaviors, presenting both compulsive-like as well as tic-like behaviors. Consistent with clinical and fundamental research literature relating compulsive-like symptoms to associative cortico-striatal dysfunctions and tic-like symptoms to sensorimotor cortico-striatal dysfunctions, abnormalities comprising both circuits have been described in this mouse model. Findings reported on these mice point towards not only macro-, but also micro-circuitry deficits, both of which can be affected by neuronal structural changes. As such, in the present study, we aimed to investigate structural changes in associative and sensorimotor striatal areas that could affect information conduction. We used AxonDeepSeg, an open-source software to automatically segment and measure myelin thickness and axon caliber, and found that axon caliber, the main contributor for changes in conduction speed, is specifically reduced in the associative but not the sensorimotor striatum of the Sapap3-KO mouse. This smaller axon caliber in Sapap3-KO mice is not a general neuronal feature of this region, but specific to a subpopulation of axons with large caliber. These results point to a primary structural deficit in the associative striatum, affecting signal conduction and consequent connectivity.
2021-10-13
Brain Sciences (publié)
doi.org
Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers
Gabriele Prato
Simon Guiroy
Ethan Caballero
Irina Rish
Sarath Chandar
Empirical science of neural scaling laws is a rapidly growing area of significant importance to the future of machine learning, particularly… (voir plus) in the light of recent breakthroughs achieved by large-scale pre-trained models such as GPT-3, CLIP and DALL-e. Accurately predicting the neural network performance with increasing resources such as data, compute and model size provides a more comprehensive evaluation of different approaches across multiple scales, as opposed to traditional point-wise comparisons of fixed-size models on fixed-size benchmarks, and, most importantly, allows for focus on the best-scaling, and thus most promising in the future, approaches. In this work, we consider a challenging problem of few-shot learning in image classification, especially when the target data distribution in the few-shot phase is different from the source, training, data distribution, in a sense that it includes new image classes not encountered during training. Our current main goal is to investigate how the amount of pre-training data affects the few-shot generalization performance of standard image classifiers. Our key observations are that (1) such performance improvements are well-approximated by power laws (linear log-log plots) as the training set size increases, (2) this applies to both cases of target data coming from either the same or from a different domain (i.e., new classes) as the training data, and (3) few-shot performance on new classes converges at a faster rate than the standard classification performance on previously seen classes. Our findings shed new light on the relationship between scale and generalization.
2021-10-12
arXiv (prépublication)
doi.org
openreview.net
Diagnosing as autistic people increasingly distant from prototypes lead neither to clinical benefit nor to the advancement of knowledge
Laurent Mottron
Danilo Bzdok
2021-10-11
Molecular Psychiatry (publié)
doi.org
ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models
Salva Rühling Cachay
Venkatesh Ramesh
Jason N. S. Cole
Howard Barker
David Rolnick
Numerical simulations of Earth's weather and climate require substantial amounts of computation. This has led to a growing interest in repla… (voir plus)cing subroutines that explicitly compute physical processes with approximate machine learning (ML) methods that are fast at inference time. Within weather and climate models, atmospheric radiative transfer (RT) calculations are especially expensive. This has made them a popular target for neural network-based emulators. However, prior work is hard to compare due to the lack of a comprehensive dataset and standardized best practices for ML benchmarking. To fill this gap, we build a large dataset, ClimART, with more than 10 million samples from present, pre-industrial, and future climate conditions, based on the Canadian Earth System Model. ClimART poses several methodological challenges for the ML community, such as multiple out-of-distribution test sets, underlying domain physics, and a trade-off between accuracy and inference speed. We also present several novel baselines that indicate shortcomings of datasets and network architectures used in prior work.
2021-10-10
NeurIPS.cc/2021/Track/Datasets_and_Benchmarks/Round2 (accepté)
openreview.net
openreview.net
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
Nan Rosemary Ke
Aniket Didolkar
Sarthak Mittal
Anirudh Goyal
Guillaume Lajoie
Stefan Bauer
Danilo Rezende
Yoshua Bengio
Michael Mozer
Christopher Pal
Inducing causal relationships from observations is a classic problem in machine learning. Most work in causality starts from the premise tha… (voir plus)t the causal variables themselves are observed. However, for AI agents such as robots trying to make sense of their environment, the only observables are low-level variables like pixels in images. To generalize well, an agent must induce high-level variables, particularly those which are causal or are affected by causal variables. A central goal for AI and causality is thus the joint discovery of abstract representations and causal structure. However, we note that existing environments for studying causal induction are poorly suited for this objective because they have complicated task-specific causal graphs which are impossible to manipulate parametrically (e.g., number of nodes, sparsity, causal chain length, etc.). In this work, our goal is to facilitate research in learning representations of high-level variables as well as causal structures among them. In order to systematically probe the ability of methods to identify these variables and structures, we design a suite of benchmarking RL environments. We evaluate various representation learning algorithms from the literature and find that explicitly incorporating structure and modularity in models can help causal induction in model-based reinforcement learning.
2021-10-10
Datasets and Benchmarks Track @ Neural Information Processing Systems (accepté)
doi.org
openreview.net
DoMoBOT: An AI-Empowered Bot for Automated and Interactive Domain Modelling
Rijul Saini
Gunter Mussbacher
Jin L.C. Guo
Jörg Kienzle
Domain modelling transforms informal requirements written in natural language in the form of problem descriptions into concise and analyzabl… (voir plus)e domain models. As the manual construction of these domain models is often time-consuming, error-prone, and labor-intensive, several approaches already exist to automate domain modelling. However, the current approaches suffer from lower accuracy of extracted domain models and the lack of support for system-modeller interactions. To better assist modellers, we introduce DoMoBOT, a web-based Domain Modelling BOT. Our proposed bot combines artificial intelligence techniques such as natural language processing and machine learning to extract domain models with higher accuracy. More importantly, our bot incorporates a set of features to bring synergy between automated model extraction and bot-modeller interactions. During these interactions, the bot presents multiple possible solutions to a modeller for modelling scenarios present in a given problem description. The bot further enables modellers to switch to a particular solution and updates the other parts of the domain model proactively. In this tool demo paper, we demonstrate how the implementation and architecture of DoMoBOT support the paradigm of automated and interactive domain modelling for assisting modellers.
2021-10-09
2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) (publié)
doi.org