Publications

Recall as a Measure of Ranking Robustness

Bhaskar Mitra

2023-01-01

arXiv.org (preprint)

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 (published)

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 Bellec

Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) analyses. Many viable str… (see more)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.

2023-01-01

(published)

www.semanticscholar.org

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) (published)

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… (see more)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 (published)

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… (see more)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 (published)

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 (preprint)

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 (published)

proceedings.mlr.press

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 (preprint)

doi.org

Screening methods for congenital anomalies in low and lower-middle income countries: A systematic review.

Justina O. Seyi-Olajide

Xiya Ma

Elena Guadagno

Adesoji Ademuyiwa

Dan Poenaru

2023-01-01

Journal of Pediatric Surgery (published)

doi.org

Self-Influence Guided Data Reweighting for Language Model Pre-training

Megh Thakkar

Tolga Bolukbasi

Sriram Ganapathy

Shikhar Vashishth

Sarath Chandar

Partha Talukdar

Language Models (LMs) pre-trained with selfsupervision on large text corpora have become the default starting point for developing models fo… (see more)r various NLP tasks. Once the pre-training corpus has been assembled, all data samples in the corpus are treated with equal importance during LM pre-training. However, due to varying levels of relevance and quality of data, equal importance to all the data samples may not be the optimal choice. While data reweighting has been explored in the context of task-specific supervised learning and LM fine-tuning, model-driven reweighting for pretraining data has not been explored. We fill this important gap and propose PRESENCE, a method for jointly reweighting samples by leveraging self-influence (SI) scores as an indicator of sample importance and pre-training. PRESENCE promotes novelty and stability for model pre-training. Through extensive analysis spanning multiple model sizes, datasets, and tasks, we present PRESENCE as an important first step in the research direction of sample reweighting for pre-training language models.

2023-01-01

EMNLP (published)

doi.org

openreview.net

SORBETmatcher results for OAEI 2023.

Francis Gosselin

Amal Zouaq

2023-01-01

OM@ISWC (published)

dblp.uni-trier.de