Publications

The Topic Confusion Task: A Novel Evaluation Scenario for Authorship Attribution
Malik H. Altakrori
Jackie CK Cheung
Benjamin C. M. Fung
2021-10-31
Findings of the Association for Computational Linguistics: EMNLP 2021 (published)
doi.org
arxiv.org
Visually Grounded Reasoning across Languages and Cultures
Fangyu Liu
Emanuele Bugliarello
Edoardo Ponti
Siva Reddy
Nigel Collier
Desmond Elliott
The design of widespread vision-and-language datasets and pre-trained encoders directly adopts, or draws inspiration from, the concepts and … (see more)images of ImageNet. While one can hardly overestimate how much this benchmark contributed to progress in computer vision, it is mostly derived from lexical databases and image queries in English, resulting in source material with a North American or Western European bias. Therefore, we devise a new protocol to construct an ImageNet-style hierarchy representative of more languages and cultures. In particular, we let the selection of both concepts and images be entirely driven by native speakers, rather than scraping them automatically. Specifically, we focus on a typologically diverse set of languages, namely, Indonesian, Mandarin Chinese, Swahili, Tamil, and Turkish. On top of the concepts and images obtained through this new protocol, we create a multilingual dataset for Multicultural Reasoning over Vision and Language (MaRVL) by eliciting statements from native speaker annotators about pairs of images. The task consists of discriminating whether each grounded statement is true or false. We establish a series of baselines using state-of-the-art models and find that their cross-lingual transfer performance lags dramatically behind supervised performance in English. These results invite us to reassess the robustness and accuracy of current state-of-the-art models beyond a narrow domain, but also open up new exciting challenges for the development of truly multilingual and multicultural systems.
2021-10-31
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (published)
doi.org
openreview.net
THE EFFECT SIZE OF GENES ON COGNITIVE ABILITIES IS LINKED TO THEIR EXPRESSION ALONG THE MAJOR HIERARCHICAL GRADIENT IN THE HUMAN BRAIN
Sébastien Jacquemont
Guillaume Huguet
Élise Douard
Zohra Saci
Guillaume Dumas
Laura Almasy
David C. Glahn
2021-10-30
European Neuropsychopharmacology (published)
doi.org
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence
Nicholas Roy
Ingmar Posner
T. Barfoot
Philippe Beaudoin
Yoshua Bengio
Jeannette Bohg
Oliver Brock
Isabelle Depatie
Dieter Fox
D. Koditschek
Tom'as Lozano-p'erez
Vikash K. Mansinghka
Christopher Pal
Blake Aaron Richards
Dorsa Sadigh
Stefan Schaal
G. Sukhatme
Denis Therien
Marc Emile Toussaint
Michiel van de Panne
2021-10-27
ArXiv (preprint)
arxiv.org
arxiv.org
How do AI systems fail socially?: an engineering risk analysis approach
Shalaleh Rismani
AJung Moon
Failure Mode and Effect Analysis (FMEA) has been used as an engineering risk assessment tool since 1949. FMEAs are effective in preemptively… (see more) identifying and addressing how a device or process might fail in operation and are often used in the design of high-risk technology applications such as military, automotive industry and medical devices. In this work, we explore whether FMEAs can serve as a risk assessment tool for machine learning practitioners, especially in deploying systems for high-risk applications (e.g. algorithms for recidivism assessment). In particular, we discuss how FMEAs can be used to identify social and ethical failures of Artificial Intelligent Systemss (AISs), recognizing that FMEAs have the potential to uncover a broader range of failures. We first propose a process for developing a Social FMEAs (So-FMEAs) by building on the existing FMEAs framework and a recently published definition of Social Failure Modes by Millar. We then demonstrate a simple proof-of-concept, So-FMEAs for the COMPAS algorithm, a risk assessment tool used by judges to make recidivism-related decisions for convicted individuals. Through this preliminary investigation, we illustrate how a traditional engineering risk management tool could be adapted for analyzing social and ethical failures of AIS. Engineers and designers of AISs can use this new approach to improve their system's design and perform due diligence with respect to potential ethical and social failures.
2021-10-27
2021 IEEE International Symposium on Ethics in Engineering, Science and Technology (ETHICS) (published)
doi.org
Rademacher Random Projections with Tensor Networks
Beheshteh T. Rakhshan
Guillaume Rabusseau
Random projection (RP) have recently emerged as popular techniques in the machine learning community for their ability in reducing the dimen… (see more)sion of very high-dimensional tensors. Following the work in [30], we consider a tensorized random projection relying on Tensor Train (TT) decomposition where each element of the core tensors is drawn from a Rademacher distribution. Our theoretical results reveal that the Gaussian low-rank tensor represented in compressed form in TT format in [30] can be replaced by a TT tensor with core elements drawn from a Rademacher distribution with the same embedding size. Experiments on synthetic data demonstrate that tensorized Rademacher RP can outperform the tensorized Gaussian RP studied in [30]. In addition, we show both theoretically and experimentally, that the tensorized RP in the Matrix Product Operator (MPO) format is not a Johnson-Lindenstrauss transform (JLT) and therefore not a well-suited random projection map
2021-10-25
ArXiv (preprint)
arxiv.org
arxiv.org
Data-Driven Learning of Geometric Scattering Modules for GNNs
Alexander Tong
Frederick Wenkel
Kincaid MacDonald
Smita Krishnaswamy
Guy Wolf
We propose a new graph neural network (GNN) module, based on relaxations of recently proposed geometric scattering transforms, which consist… (see more) of a cascade of graph wavelet filters. Our learnable geometric scattering (LEGS) module enables adaptive tuning of the wavelets to encourage band-pass features to emerge in learned representations. The incorporation of our LEGS-module in GNNs enables the learning of longer-range graph relations compared to many popular GNNs, which often rely on encoding graph structure via smoothness or similarity between neighbors. Further, its wavelet priors result in simplified architectures with significantly fewer learned parameters compared to competing GNNs. We demonstrate the predictive performance of LEGS-based networks on graph classification benchmarks, as well as the descriptive quality of their learned features in biochemical graph data exploration tasks.
2021-10-24
PubMed Central (accepted)
doi.org
arxiv.org
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.… (see more) 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 (preprint)
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… (see more)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 (accepted)
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 … (see 19 more)
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… (see more)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 (published)
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 (preprint)
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… (see more) 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) (preprint)
doi.org
arxiv.org