Publications

Managing AI Risks in an Era of Rapid Progress
Yoshua Bengio
Geoffrey Hinton
Andrew Yao
Dawn Song
Pieter Abbeel
Trevor Darrell
Yuval Noah Harari
Ya-Qin Zhang
Lan Xue
Shai Shalev-Shwartz
Gillian K. Hadfield
Jeff Clune
Tegan Maharaj
Frank Hutter
Atilim Güneş Baydin
Sheila McIlraith
Qiqi Gao
Ashwin Acharya
David Scott Krueger
Anca Dragan … (voir 5 de plus)
Philip Torr
Stuart Russell
Daniel Kahneman
Jan Brauner
Sören Mindermann
2023-10-26
Science (publié)
doi.org
arxiv.org
Surrogate Minimization: An Optimization Algorithm for Training Large Neural Networks with Model Parallelism
Reza Asad
Reza Babanezhad Harikandeh
Issam Hadj Laradji
Nicolas Le Roux
Sharan Vaswani
2023-10-26
NeurIPS.cc/2023/Workshop/OPT (poster)
openreview.net
openreview.net
The value of standards for health datasets in artificial intelligence-based applications
Anmol Arora
Joseph E. Alderman
Joanne Palmer
Shaswath Ganapathi
Elinor Laws
Melissa D. McCradden
Lauren Oakden-Rayner
Stephen R. Pfohl
Marzyeh Ghassemi
Francis McKay
Darren Treanor
Negar Rostamzadeh
Bilal Mateen
Jacqui Gath
Adewole O. Adebajo
Stephanie Kuku
Rubeta Matin
Katherine Heller
Elizabeth Sapey
Neil J. Sebire … (voir 4 de plus)
Heather Cole-Lewis
Melanie Calvert
Alastair Denniston
Xiaoxuan Liu
2023-10-26
Nature Medicine (publié)
doi.org
CL-MASR: A Continual Learning Benchmark for Multilingual ASR
Luca Della Libera
Pooneh Mousavi
Salah Zaiem
Cem (Yusuf) Subakan
Mirco Ravanelli
2023-10-25
ArXiv (prépublication)
doi.org
arxiv.org
PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design
Chuanrui Wang
Bozitao Zhong
Zuobai Zhang
Narendra Chaudhary
Sanchit Misra
Jian Tang
Structure-based protein design has attracted increasing interest, with numerous methods being introduced in recent years. However, a univers… (voir plus)ally accepted method for evaluation has not been established, since the wet-lab validation can be overly time-consuming for the development of new algorithms, and the
2023-10-25
NeurIPS.cc/2023/Workshop/AI4D3 (poster)
openreview.net
openreview.net
PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design
Chuanrui Wang
Bozitao Zhong
Zuobai Zhang
Narendra Chaudhary
Sanchit Misra
Jian Tang
Structure-based protein design has attracted increasing interest, with numerous methods being introduced in recent years. However, a univers… (voir plus)ally accepted method for evaluation has not been established, since the wet-lab validation can be overly time-consuming for the development of new algorithms, and the
2023-10-25
NeurIPS.cc/2023/Workshop/AI4D3 (poster)
openreview.net
openreview.net
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.
2023-10-25
SIAM Journal on Mathematics of Data Science (publié)
doi.org
arxiv.org
Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles
Xing Shen
Hengguan Huang
Brennan Nichyporuk
Tal Arbel
2023-10-24
ArXiv (prépublication)
doi.org
arxiv.org
Validation of ANG-1 and P-SEL as biomarkers of post-COVID-19 conditions using data from the Biobanque québécoise de la COVID-19 (BQC-19)
Eric Yamga
Antoine Soulé
Alain Piché
Amin Emad
Madeleine Durand
Simon Rousseau
2023-10-24
Clinical Proteomics (publié)
doi.org
Causal machine learning for single-cell genomics
Alejandro Tejada-Lapuerta
Paul Bertin
Stefan Bauer
Hananeh Aliee
Yoshua Bengio
Fabian J. Theis
2023-10-23
ArXiv (prépublication)
doi.org
arxiv.org
Distributional Robustness and Inequity Mitigation in Disaster Preparedness of Humanitarian Operations
Hongming Li
Érick Delage
Ning Zhu
Michael Pinedo
Shoufeng Ma
Problem definition: In this paper, we study a predisaster relief network design problem with uncertain demands. The aim is to determine the … (voir plus)prepositioning and reallocation of relief supplies. Motivated by the call of the International Federation of Red Cross and Red Crescent Societies (IFRC) to leave no one behind, we consider three important practical aspects of humanitarian operations: shortages, equity, and uncertainty. Methodology/results: We first employ a form of robust satisficing measure, which we call the shortage severity measure, to evaluate the severity of the shortage caused by uncertain demand in a context with limited distribution information. Because shortages often raise concerns about equity, we then formulate a mixed-integer lexicographic optimization problem with nonconvex objectives and design a new branch-and-bound algorithm to identify the exact solution. We also propose two approaches for identifying optimal postdisaster adaptable resource reallocation: an exact approach and a conservative approximation that is more computationally efficient. Our case study considers the 2010 Yushu earthquake, which occurred in northwestern China, and demonstrates the value of our methodology in mitigating geographical inequities and reducing shortages. Managerial implications: In our case study, we show that (i) incorporating equity in both predisaster deployment and postdisaster reallocation can produce substantially more equitable shortage prevention strategies while sacrificing only a reasonable amount of total shortage; (ii) increasing donations/budgets may not necessarily alleviate the shortage suffered by the most vulnerable individuals if equity is not fully considered; and (iii) exploiting disaster magnitude information when quantifying uncertainty can help alleviate geographical inequities caused by uncertain relief demands. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2016-05208], the National Natural Science Foundation of China [Grants 71971154, 72010107004, 72091214, and 72122015], and the Canada Research Chairs [Grant CRC-2018-00105]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1230 .
2023-10-23
Manufacturing & Service Operations Management (publié)
doi.org
Dosimetry of [18F]TRACK, the first PET tracer for imaging of TrkB/C receptors in humans
Alexander Thiel
Alexey Kostikov
Hailey Ahn
Youstina Daoud
Jean-Paul Soucy
Stephan Blinder
Carolin Jaworski
Carmen Wängler
Björn Wängler
Freimut Juengling
Shirin A. Enger
Ralf Schirrmacher
2023-10-23
EJNMMI Radiopharmacy and Chemistry (publié)
doi.org