Publications

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… (see more)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

(published)

www.semanticscholar.org

Ultrastructure Analysis of Cardiomyocytes and Their Nuclei

Tabish A Syed

Yanan Wang

Drisya Dileep

Minhajuddin Sirajuddin

Kaleem Siddiqi

2022-12-31

Functional Imaging and Modeling of the Heart (published)

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… (see more)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 (published)

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… (see more)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 (published)

Unsupervised Layer-wise Score Aggregation for Textual OOD Detection

Maxime Darrin

Guillaume Staerman

Eduardo DC GOMEZ

Jackie CK Cheung

Pablo Piantanida

Pierre Colombo

Out-of-distribution (OOD) detection is a rapidly growing field due to new robustness and security requirements driven by an increased number… (see more) of AI-based systems. Existing OOD textual detectors often rely on an anomaly score (e.g., Mahalanobis distance) computed on the embedding output of the last layer of the encoder. In this work, we observe that OOD detection performance varies greatly depending on the task and layer output. More importantly, we show that the usual choice (the last layer) is rarely the best one for OOD detection and that far better results could be achieved if the best layer were picked. To leverage this observation, we propose a data-driven, unsupervised method to combine layer-wise anomaly scores. In addition, we extend classical textual OOD benchmarks by including classification tasks with a greater number of classes (up to 77), which reflects more realistic settings. On this augmented benchmark, we show that the proposed post-aggregation methods achieve robust and consistent results while removing manual feature selection altogether. Their performance achieves near oracle's best layer performance.

2022-12-31

arXiv (preprint)

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 (published)

Using Confounded Data in Latent Model-Based Reinforcement Learning

Maxime Gasse

Damien GRASSET

Guillaume Gaudron

Pierre-Yves Oudeyer

2022-12-31

Trans. Mach. Learn. Res. (published)

Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top

Eduard Gorbunov

Samuel Horváth

Peter Richtárik

Gauthier Gidel

Byzantine-robustness has been gaining a lot of attention due to the growth of the interest in collaborative and federated learning. However,… (see more) many fruitful directions, such as the usage of variance reduction for achieving robustness and communication compression for reducing communication costs, remain weakly explored in the field. This work addresses this gap and proposes Byz-VR-MARINA - a new Byzantine-tolerant method with variance reduction and compression. A key message of our paper is that variance reduction is key to fighting Byzantine workers more effectively. At the same time, communication compression is a bonus that makes the process more communication efficient. We derive theoretical convergence guarantees for Byz-VR-MARINA outperforming previous state-of-the-art for general non-convex and Polyak-Lojasiewicz loss functions. Unlike the concurrent Byzantine-robust methods with variance reduction and/or compression, our complexity results are tight and do not rely on restrictive assumptions such as boundedness of the gradients or limited compression. Moreover, we provide the first analysis of a Byzantine-tolerant method supporting non-uniform sampling of stochastic gradients. Numerical experiments corroborate our theoretical findings.

2022-12-31

International Conference on Learning Representations (published)

Versatile Energy-Based Probabilistic Models for High Energy Physics

Taoli Cheng

Aaron Courville

As a classical generative modeling approach, energy-based models have the natural advantage of flexibility in the form of the energy functio… (see more)n. Recently, energy-based models have achieved great success in modeling high-dimensional data in computer vision and natural language processing. In line with these advancements, we build a multi-purpose energy-based probabilistic model for High Energy Physics events at the Large Hadron Collider. This framework builds on a powerful generative model and describes higher-order inter-particle interactions. It suits different encoding architectures and builds on implicit generation. As for applicative aspects, it can serve as a powerful parameterized event generator for physics simulation, a generic anomalous signal detector free from spurious correlations, and an augmented event classifier for particle identification.

2022-12-31

arXiv.org (preprint)

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… (see more)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) (published)

Willingness to Engage in Shared Decision Making: Impact of an Educational Intervention for Resident Physicians (SDM-FM)

Roland M. Grad

A. Sandhu

Michael Ferrante

Vinita D'souza

Lily Puterman-salzman

S. A. Rahimi

Gabrielle Stevens

G. Elwyn

2022-12-31

Education and training (published)