Publications

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
Assessing Intrapartum Risk of Hypoxic-Ischemic Encephalopathy using Fetal Heart Rate with Long Short-term Memory Networks
"Derek Kweku DEGBEDZUI
Michael Kuzniewicz
Cornet Marie-Coralie
Yvonne Wu
Heather Forquer
Lawrence Gerstley
Emily Hamilton
Doina Precup
Philip Warrick
Robert Kearney"
This study investigated the prediction of the risk of hypoxic ischemic encephalopathy using intrapartum cardiotocography records with a long… (voir plus) short-term memory re-current neural network. Across the 12 hours of labour, HIE sensitivity rose from 0.25 to 0.56 as delivery approached while specificity remained approximately constant with a mean of 0.71 and standard deviation of 0.04. The results show that classification improves as delivery approaches but that performance needs improvement. Future work will address the limitations of this preliminary study by investigating input signal transformations and the use of other network architectures to improve the model performance.
2022-12-30
Computing in Cardiology Conference (CinC) (publié)
doi.org
Offline Policy Optimization in RL with Variance Regularizaton
Riashat Islam
Samarth Sinha
Homanga Bharadhwaj
Samin Yeasar Arnob
Zhuoran Yang
Animesh Garg
Zhaoran Wang
Lihong Li
Doina Precup
2022-12-28
arXiv (prépublication)
doi.org
arxiv.org
Orientation and Context Entangled Network for Retinal Vessel Segmentation
Xinxu Wei
Kaifu Yang
Danilo Bzdok
Yongjie Li
Most of the existing deep learning based methods for vessel segmentation neglect two important aspects of retinal vessels, one is the orient… (voir plus)ation information of vessels, and the other is the contextual information of the whole fundus region. In this paper, we propose a robust Orientation and Context Entangled Network (denoted as OCE-Net), which has the capability of extracting complex orientation and context information of the blood vessels. To achieve complex orientation aware, a Dynamic Complex Orientation Aware Convolution (DCOA Conv) is proposed to extract complex vessels with multiple orientations for improving the vessel continuity. To simultaneously capture the global context information and emphasize the important local information, a Global and Local Fusion Module (GLFM) is developed to simultaneously model the long-range dependency of vessels and focus sufficient attention on local thin vessels. A novel Orientation and Context Entangled Non-local (OCE-NL) module is proposed to entangle the orientation and context information together. In addition, an Unbalanced Attention Refining Module (UARM) is proposed to deal with the unbalanced pixel numbers of background, thick and thin vessels. Extensive experiments were performed on several commonly used datasets (DRIVE, STARE and CHASEDB1) and some more challenging datasets (AV-WIDE, UoA-DR, RFMiD and UK Biobank). The ablation study shows that the proposed method achieves promising performance on maintaining the continuity of thin vessels and the comparative experiments demonstrate that our OCE-Net can achieve state-of-the-art performance on retinal vessel segmentation.
2022-12-28
Expert Systems with Applications (inconnu)
doi.org
arxiv.org
Picture Cards Versus Physical Examination: A Proof-of-Concept Study to Improve the SOSAS Survey Tool.
Adesoji Ademuyiwa
Benedict C. Nwomeh
Dan Poenaru
Justina O. Seyi-Olajide
Iyabo Y. Ademuyiwa
Tinuola O. Odugbemi
Ogechi Abazie
Oluwaseun A. Ladipo-Ajayi
Olufemi Bankole
Olumide A. Elebute
Babasola Okusanya
Felix M. Alakaloko
Eyitayo O. Alabi
Ayomide Makanjuola
Shailvi Gupta
Tu Tran
Amanda Onwuka A
Emily R. Smith
Riinu Pius
Ewen Harrison … (voir 1 de plus)
Christopher O. Bode
2022-12-26
Journal of Surgical Research (publié)
doi.org
Simulation-based power analysis could improve the design of clinical trials in Alzheimer's disease
Daniel Andrews
Douglas L. Arnold
Danilo Bzdok
Simon Ducharme
Howard Chertkow
D. Louis Collins
Clinical trials of new treatments in different progressive diseases use power analysis to determine the sample size needed for a trial to ob… (voir plus)tain a statistically significant estimate for an anticipated treatment effect. In trials with parallel designs, the standard power analysis approach is based on a two-sample t-test. For example, the standard t-test approach was used in determining the sample size for the Phase 3 trials of aducanumab, the first drug approved by the United States Food and Drug Administration (FDA) to potentially slow cognitive decline in early-stage Alzheimer’s disease. However, t-tests contain normality assumptions, and t-test-based power analyses do not implicitly factor in the uncertainty about anticipated treatment effects that arises due to inter-subject heterogeneity in disease progression. These limitations may lead to recommended sample sizes that are too small, potentially making a trial blind to a treatment effect that is truly present if the cohort’s endpoints are not normally distributed and/or the anticipated treatment effect is overestimated. To address these issues, we present a novel power analysis method that (1) simulates clinical trials in a progressive disease using real-world data, (2) accounts for inter-subject heterogeneity in disease progression, and (3) does not depend on normality assumptions. As a showcase example, we used our method to calculate power for a range of sample sizes and treatment effects in simulated trials similar to the Phase 3 aducanumab trials EMERGE and ENGAGE. As expected, our results show that power increases with number of subjects and treatment effect (here defined as the cohort-level percent reduction in the rate of cognitive decline in treated subjects vs. controls). However, inclusion of realistic inter-subject heterogeneity in cognitive decline trajectories leads to increased sample size recommendations compared to a standard t-test power analysis. These results suggest that the sample sizes recommended by the t-test power analyses in the EMERGE and ENGAGE Statistical Analysis Plans were possibly too small to ensure a high probability of detecting the anticipated treatment effect. Insufficient sample sizes could partly explain the statistically significant effect of aducanumab being detected only in EMERGE. We also used our method to analyze power in simulated trials similar the Phase 3 lecanemab trial Clarity AD. Our results suggest that Clarity AD was adequately powered, and that power may be influenced by a trial’s number of analysis visits and the characteristics of subgroups within a cohort. By using our simulation-based power analysis approach, clinical trials of treatments in Alzheimer’s disease and potentially in other progressive diseases could obtain sample size recommendations that account for heterogeneity in disease progression and uncertainty in anticipated treatment effects. Our approach avoids the limitations of t-tests and thus could help ensure that clinical trials are more adequately powered to detect the treatment effects they seek to measure.
2022-12-26
medRxiv (prépublication)
doi.org
How programmers find online learning resources
Deeksha M. Arya
Jin L.C. Guo
Martin P. Robillard
2022-12-23
Empirical Software Engineering (publié)
doi.org