Publications

On the Limitations of Elo: Real-World Games, are Transitive, not Additive
Quentin Bertrand
Wojciech M. Czarnecki
Gauthier Gidel
Real-world competitive games, such as chess, go, or StarCraft II, rely on Elo models to measure the strength of their players. Since these g… (voir plus)ames are not fully transitive, using Elo implicitly assumes they have a strong transitive component that can correctly be identified and extracted. In this study, we investigate the challenge of identifying the strength of the transitive component in games. First, we show that Elo models can fail to extract this transitive component, even in elementary transitive games. Then, based on this observation, we propose an extension of the Elo score: we end up with a disc ranking system that assigns each player two scores, which we refer to as skill and consistency. Finally, we propose an empirical validation on payoff matrices coming from real-world games played by bots and humans.
2022-12-31
AISTATS (publié)
doi.org
arxiv.org
The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation
Dung Nguyen Manh
Nam Le Hai
Anh T. V. Dau
Anh Minh Nguyen
Khanh Nghiem
Jin Guo
Nghi D. Q. Bui
Dung Nguyen Manh, Nam Le Hai, Anh T. V. Dau, Anh Minh Nguyen, Khanh Nghiem, Jin Guo, Nghi D. Q. Bui. Proceedings of the 3rd Workshop for Nat… (voir plus)ural Language Processing Open Source Software (NLP-OSS 2023). 2023.
2022-12-31
Workshop for Natural Language Processing Open Source Software (publié)
doi.org
Time-inhomogeneous diffusion geometry and topology
Guillaume Huguet
Alexander Tong
Bastian Rieck
Jessie Huang
Manik Kuchroo
Matthew Hirn
Guy Wolf
Smita Krishnaswamy
Diffusion condensation is a dynamic process that yields a sequence of multiscale data representations that aim to encode meaningful abstract… (voir plus)ions. It has proven effective for manifold learning, denoising, clustering, and visualization of high-dimensional data. Diffusion condensation is constructed as a time-inhomogeneous process where each step first computes and then applies a diffusion operator to the data. We theoretically analyze the convergence and evolution of this process from geometric, spectral, and topological perspectives. From a geometric perspective, we obtain convergence bounds based on the smallest transition probability and the radius of the data, whereas from a spectral perspective, our bounds are based on the eigenspectrum of the diffusion kernel. Our spectral results are of particular interest since most of the literature on data diffusion is focused on homogeneous processes. From a topological perspective, we show diffusion condensation generalizes centroid-based hierarchical clustering. We use this perspective to obtain a bound based on the number of data points, independent of their location. To understand the evolution of the data geometry beyond convergence, we use topological data analysis. We show that the condensation process itself defines an intrinsic condensation homology. We use this intrinsic topology as well as the ambient persistent homology of the condensation process to study how the data changes over diffusion time. We demonstrate both types of topological information in well-understood toy examples. Our work gives theoretical insights into the convergence of diffusion condensation, and shows that it provides a link between topological and geometric data analysis.
2022-12-31
SIAM Journal on Mathematics of Data Science (publié)
doi.org
arxiv.org
Toward computing attributions for dimensionality reduction techniques
Matthew Scicluna
Jean-Christophe Grenier
Raphaël Poujol
Sébastien Lemieux
Julie G. Hussin
We describe the problem of computing local feature attributions for dimensionality reduction methods. We use one such method that is well es… (voir plus)tablished within the context of supervised classification—using the gradients of target outputs with respect to the inputs—on the popular dimensionality reduction technique t-SNE, widely used in analyses of biological data. We provide an efficient implementation for the gradient computation for this dimensionality reduction technique. We show that our explanations identify significant features using novel validation methodology; using synthetic datasets and the popular MNIST benchmark dataset. We then demonstrate the practical utility of our algorithm by showing that it can produce explanations that agree with domain knowledge on a SARS-CoV-2 sequence dataset. Throughout, we provide a road map so that similar explanation methods could be applied to other dimensionality reduction techniques to rigorously analyze biological datasets. We have created a Python package that can be installed using the following command: pip install interpretable_tsne. All code used can be found at github.com/MattScicluna/interpretable_tsne.
2022-12-31
Bioinformatics Advances (publié)
doi.org
Transposable elements regulate thymus development and function 1
Jean-David Larouche
Céline M. Laumont
Assya Trofimov
Krystel Vincent
Leslie Hesnard
Sylvie Brochu
Caroline Coté
Juliette Humeau
Éric Bonneil
Joël Lanoix
Chantal Durette
Patrick Gendron
Jean-Philippe Laverdure
Ellen Rothman Richie
S. Lemieux
Pierre Thibault
Claude Perreault
21 Transposable elements (TE) are repetitive sequences representing ~45% of the human and mouse genomes 22 and are highly expressed by medul… (voir plus)lary thymic epithelial cells (mTEC). In this study, we investigated the 23 role of transposable elements (TE), which are highly expressed by medullary thymic epithelial cells 24 (mTEC), on T-cell development in the thymus. We performed multi-omic analyses of TEs in human and 25 mouse thymic cells to elucidate their role in T cell development. We report that TE expression in the 26 human thymus is high and shows extensive ageand cell lineage-related variations. TEs interact with 27 multiple transcription factors in all cell types of the human thymus. Two cell types express particularly 28 broad TE repertoires: mTECs and plasmacytoid dendritic cells (pDC). In mTECs, TEs interact with 29 transcription factors essential for mTEC development and function (e.g., PAX1 and RELB) and generate 30 MHC-I-associated peptides implicated in thymocyte education. Notably, AIRE, FEZF2, and CHD4 31 regulate non-redundant sets of TEs in murine mTECs. Human thymic pDCs homogenously express large 32 numbers of TEs that lead to the formation of dsRNA, triggering RIG-I and MDA5 signaling and 33 explaining why thymic pDCs constitutively secrete IFN ɑ/β. This study illustrates the diversity of 34 interactions between TEs and the adaptive immune system. TEs are genetic parasites, and the two thymic 35 cell types most affected by TEs (mTEcs and pDCs) are essential to establishing central T-cell tolerance. 36 Therefore, we propose that the orchestration of TE expression in thymic cells is critical to prevent 37 autoimmunity in vertebrates. 38
2022-12-31
(publié)
www.semanticscholar.org
Trophic interaction models predict interactions across space, not food webs.
Dominique Caron
Ulrich Brose
Miguel Lurgi
F. Guillaume Blanchet
Dominique Gravel
Laura J. Pollock
Aim: Trophic interactions are central to our understanding of essential ecosystem functions as well as their stability. Predicting these int… (voir plus)eractions has become increasingly common due to the lack of empirical data on trophic interactions for most taxa in most ecosystems. We aim to determine how far and accurately trophic interaction models extrapolate to new communities both in terms of pairwise predator-prey interactions and higher level food web attributes (i.e., species position, food web-level properties).
2022-12-31
(publié)
www.semanticscholar.org
Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms
Michael Perlmutter
Alexander Tong
Feng Gao
Guy Wolf
Matthew Hirn
The scattering transform is a multilayered wavelet-based deep learning architecture that acts as a model of convolutional neural networks. R… (voir plus)ecently, several works have introduced generalizations of the scattering transform for non-Euclidean settings such as graphs. Our work builds upon these constructions by introducing windowed and non-windowed geometric scattering transforms for graphs based upon a very general class of asymmetric wavelets. We show that these asymmetric graph scattering transforms have many of the same theoretical guarantees as their symmetric counterparts. As a result, the proposed construction unifies and extends known theoretical results for many of the existing graph scattering architectures. In doing so, this work helps bridge the gap between geometric scattering and other graph neural networks by introducing a large family of networks with provable stability and invariance guarantees. These results lay the groundwork for future deep learning architectures for graph-structured data that have learned filters and also provably have desirable theoretical properties.
2022-12-31
SIAM Journal on Mathematics of Data Science (publié)
doi.org
arxiv.org
Unsupervised Improvement of Audio-Text Cross-Modal Representations
Zhepei Wang
Cem Subakan
Krishna Subramani
Junkai Wu
Tiago Tavares
Fabio Ayres
Paris Smaragdis
Recent advances in using language models to obtain cross-modal audio-text representations have overcome the limitations of conventional trai… (voir plus)ning approaches that use predefined labels. This has allowed the community to make progress in tasks like zero-shot classification, which would otherwise not be possible. However, learning such representations requires a large amount of human-annotated audio-text pairs. In this paper, we study unsupervised approaches to improve the learning framework of such representations with unpaired text and audio. We explore domain-unspecific and domain-specific curation methods to create audio-text pairs that we use to further improve the model. We also show that when domain-specific curation is used in conjunction with a soft-labeled contrastive loss, we are able to obtain significant improvement in terms of zero-shot classification performance on downstream sound event classification or acoustic scene classification tasks.
2022-12-31
WASPAA (publié)
doi.org
arxiv.org
Use of machine learning in pediatric surgical clinical prediction tools: A systematic review.
Amanda Bianco
Zaid A.M. Al-Azzawi
Elena Guadagno
Esli Osmanlliu
Jocelyn Gravel
Dan Poenaru
2022-12-31
Journal of Pediatric Surgery (publié)
doi.org
Vārta: A Large-Scale Headline-Generation Dataset for Indic Languages
Rahul Aralikatte
Ziling Cheng
Sumanth Doddapaneni
Jackie Chi Kit Cheung
We present Varta, a large-scale multilingual dataset for headline generation in Indic languages. This dataset includes more than 41 million … (voir plus)pairs of headlines and articles in 14 different Indic languages (and English), which come from a variety of high-quality news sources. To the best of our knowledge, this is the largest collection of curated news articles for Indic languages currently available. We use the collected data in a series of experiments to answer important questions related to Indic NLP and multilinguality research in general. We show that the dataset is challenging even for state-of-the-art abstractive models and that they perform only slightly better than extractive baselines. Owing to its size, we also show that the dataset can be used to pre-train strong language models that outperform competitive baselines in both NLU and NLG benchmarks.
2022-12-31
Findings of the Association for Computational Linguistics (publié)
doi.org
arxiv.org
Video Killed the HD-Map: Predicting Multi-Agent Behavior Directly From Aerial Images
Yunpeng Liu
Vasileios Lioutas
Jonathan Wilder Lavington
Matthew Niedoba
Justice Sefas
Setareh Dabiri
Dylan Green
Xiaoxuan Liang
Berend Zwartsenberg
Adam Ścibior
Frank N. Wood
The development of algorithms that learn multi-agent behavioral models using human demonstrations has led to increasingly realistic simulati… (voir plus)ons in the field of autonomous driving. In general, such models learn to jointly predict trajectories for all controlled agents by exploiting road context information such as drivable lanes obtained from manually annotated high-definition (HD) maps. Recent studies show that these models can greatly benefit from increasing the amount of human data available for training. However, the manual annotation of HD maps which is necessary for every new location puts a bottleneck on efficiently scaling up human traffic datasets. We propose an aerial image-based map (AIM) representation that requires minimal annotation and provides rich road context information for traffic agents like pedestrians and vehicles. We evaluate multi-agent trajectory prediction using the AIM by incorporating it into a differentiable driving simulator as an image-texture-based differentiable rendering module. Our results demonstrate competitive multi-agent trajectory prediction performance especially for pedestrians in the scene when using our AIM representation as compared to models trained with rasterized HD maps.
2022-12-31
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) (publié)
doi.org
arxiv.org
"Your child needs surgery": A survey-based evaluation of simulated expert consent conversations by key stakeholders.
Zoe Atsaidis
Stephan Robitaille
Elena Guadagno
Jeffrey Wiseman
Sherif Emil
Dan Poenaru
2022-12-31
Journal of Pediatric Surgery (publié)
doi.org