Publications

Publisher Correction: Advancing ethics review practices in AI research
Madhulika Srikumar
Rebecca Finlay
Grace M. Abuhamad
Carolyn Ashurst
Rosie Campbell
Emily Campbell-Ratcliffe
Hudson Hongo
Sara Rene Jordan
Joseph Lindley
Aviv Ovadya
Joelle Pineau
2023-01-01
Nature Machine Intelligence (publié)
doi.org
A rapid review for developing a co-design framework for a pediatric surgical communication application
Michelle Cwintal
Hamed Ranjbar
Parsa Bandamiri
Elena Guadagno
Esli Osmanlliu
Dan Poenaru
2023-01-01
Journal of Pediatric Surgery (publié)
doi.org
Recall as a Measure of Ranking Robustness
Fernando Diaz
Bhaskar Mitra
2023-01-01
arXiv.org (prépublication)
doi.org
Replay Buffer with Local Forgetting for Adapting to Local Environment Changes in Deep Model-Based Reinforcement Learning
Ali Rahimi-Kalahroudi
Janarthanan Rajendran
Ida Momennejad
Harm van Seijen
Sarath Chandar
2023-01-01
CoLLAs (publié)
proceedings.mlr.press
arxiv.org
A reproducible benchmark of resting-state fMRI denoising strategies using fMRIPrep and Nilearn
Hao-Ting Wang
Steven L. Meisler
Hanad Sharmarke
Natasha Clarke
Nicolas Gensollen
Christopher J Markiewicz
Fraçois Paugam
Bertrand Thirion
Lune P Bellec
Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) analyses. Many viable str… (voir plus)ategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a fully reproducible denoising benchmark featuring a range of denoising strategies and evaluation metrics, built primarily on the fMRIPrep and Nilearn software packages. We apply this reproducible benchmark to investigate the robustness of the conclusions across two different datasets and two versions of fMRIPrep. The majority of benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing however disrupts the continuous sampling of brain images and is incompatible with some statistical analyses, e.g. auto-regressive modeling. In this case, a simple strategy using motion parameters, average activity in select brain compartments, and global signal regression should be preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks, especially ICA-AROMA. These results demonstrate that a reproducible denoising benchmark can effectively assess the robustness of conclusions across multiple datasets and software versions. Technologies such as BIDS-App, the Jupyter Book and Neurolibre provided the infrastructure to publish the metadata and report figures. Readers can reproduce the report figures beyond the ones reported in the published manuscript. With the denoising benchmark, we hope to provide useful guidelines for the community, and that our software infrastructure will facilitate continued development as the state-of-the-art advances.
Responsible AI Considerations in Text Summarization Research: A Review of Current Practices
Yu Lu Liu
Meng Cao
Su Lin Blodgett
Jackie Cheung
Alexandra Olteanu
Adam Trischler
2023-01-01
EMNLP (Findings) (publié)
doi.org
openreview.net
Re-Weighted Softmax Cross-Entropy to Control Forgetting in Federated Learning
Gwen Legate
Lucas Caccia
Eugene Belilovsky
In Federated Learning, a global model is learned by aggregating model updates computed at a set of independent client nodes, to reduce commu… (voir plus)nication costs multiple gradient steps are performed at each node prior to aggregation. A key challenge in this setting is data heterogeneity across clients resulting in differing local objectives which can lead clients to overly minimize their own local objective, diverging from the global solution. We demonstrate that individual client models experience a catastrophic forgetting with respect to data from other clients and propose an efficient approach that modifies the cross-entropy objective on a per-client basis by re-weighting the softmax logits prior to computing the loss. This approach shields classes outside a client's label set from abrupt representation change and we empirically demonstrate it can alleviate client forgetting and provide consistent improvements to standard federated learning algorithms. Our method is particularly beneficial under the most challenging federated learning settings where data heterogeneity is high and client participation in each round is low.
2023-01-01
CoLLAs (publié)
doi.org
openreview.net
Re-Weighted Softmax Cross-Entropy to Control Forgetting in Federated Learning
Gwen Legate
Lucas Caccia
Eugene Belilovsky
In Federated Learning a global model is learned by aggregating model updates computed at a set of independent client nodes. To reduce commun… (voir plus)ication costs, multiple gradient steps are performed at each node prior to aggregation. A key challenge in this setting is data heterogeneity across clients resulting in differing local objectives. This can lead clients to overly minimize their own local objective consequently diverging from the global solution. We demonstrate that individual client models experience a catastrophic forgetting with respect to data from other clients and propose an efficient approach that modifies the cross-entropy objective on a per-client basis by re-weighting the softmax logits prior to computing the loss. This approach shields classes outside a client’s label set from abrupt representation change and we empirically demonstrate it can alleviate client forgetting and provide consistent improvements to standard federated learning algorithms. Our method is particularly beneficial under the most challenging federated learning settings where data heterogeneity is high and client participation in each round is low.
2023-01-01
CoLLAs (publié)
doi.org
openreview.net
Robust and Versatile Bipedal Jumping Control through Multi-Task Reinforcement Learning
Zhongyu Li
Xue Bin Peng
Pieter Abbeel
Sergey Levine
Glen Berseth
Koushil Sreenath
2023-01-01
arXiv.org (prépublication)
doi.org
Sample Boosting Algorithm (SamBA) - An interpretable greedy ensemble classifier based on local expertise for fat data
Baptiste Bauvin
Cécile Capponi
Florence Clerc
Pascal Germain
Sokol Koço
Jacques Corbeil
2023-01-01
UAI (publié)
proceedings.mlr.press
openreview.net
Scalar Invariant Networks with Zero Bias
Chuqin Geng
Xiaojie Xu
Haolin Ye
Xujie Si
Just like weights, bias terms are the learnable parameters of many popular machine learning models, including neural networks. Biases are th… (voir plus)ought to enhance the representational power of neural networks, enabling them to solve a variety of tasks in computer vision. However, we argue that biases can be disregarded for some image-related tasks such as image classification, by considering the intrinsic distribution of images in the input space and desired model properties from first principles. Our findings suggest that zero-bias neural networks can perform comparably to biased networks for practical image classification tasks. We demonstrate that zero-bias neural networks possess a valuable property called scalar (multiplication) invariance. This means that the prediction of the network remains unchanged when the contrast of the input image is altered. We extend scalar invariance to more general cases, enabling formal verification of certain convex regions of the input space. Additionally, we prove that zero-bias neural networks are fair in predicting the zero image. Unlike state-of-the-art models that may exhibit bias toward certain labels, zero-bias networks have uniform belief in all labels. We believe dropping bias terms can be considered as a geometric prior in designing neural network architecture for image classification, which shares the spirit of adapting convolutions as the transnational invariance prior. The robustness and fairness advantages of zero-bias neural networks may also indicate a promising path towards trustworthy and ethical AI.
2023-01-01
NeurReps (publié)
doi.org
openreview.net
Scaling Self-Supervised End-to-End Driving with Multi-View Attention Learning
Yi Xiao
Felipe Codevilla
Diego Porres
Antonio M. López
2023-01-01
arXiv.org (prépublication)
doi.org