Publications

A Systematic Review of the Empirical Use of the CCHS-MH (Canadian Community Health Survey–Mental Health) Survey
Maria Cutumisu
2025-03-31
Proceedings of the 2025 AERA Annual Meeting (published)
doi.org
On the Analysis and Distillation of Emergent Outlier Properties in Pre-trained Language Models
Tianyang Zhao
Kunwar Yashraj Singh
Srikar Appalaraju
Peng Tang
Ying Nian Wu
Li Erran Li
Li
Rishabh Agarwal
Nino Vieillard
Yongchao Zhou
Piotr Stańczyk
Sabela Ramos Garea
Matthieu Geist
Rohan Anil
Andrew M. Dai
Melvin Orhan Firat
Dmitry Lepikhin
Alexandre Passos
Siamak Shakeri
Emanuel Taropa … (see 478 more)
Paige Bailey
Zhifeng Chen
Eric Chu
Jonathan H. Clark
Laurent El
Yanping Huang
K. Meier-Hellstern
Gaurav Mishra
Erica Moreira
Mark Omernick
Kevin Robinson
Sebastian Ruder
Yi Tay
Kefan Xiao
Yuanzhong Xu
Yujing Zhang
Gustavo Hernández Abrego
Junwhan Ahn
Jacob Austin
Paul R. Barham
Jan Botha
James Bradbury
Siddhartha Brahma
Kevin Brooks
M. Catasta
Yong Cheng
Colin Cherry
Christopher A. Choquette-Choo
Aakanksha Chowdhery
Clé-ment Crepy
Shachi Dave
Mostafa Dehghani
Sunipa Dev
Jacob Devlin
Mark Díaz
Nan Du
Ethan Dyer
Vladimir Feinberg
Fangxiaoyu Feng
Vlad Fienber
Markus Freitag
Xavier Garcia
Sebastian Gehrmann
Lucas Gonzalez
Guy Gur-Ari
Steven Hand
Hadi Hashemi
Le Hou
Joshua Howland
Andrea Hu
Jeffrey Hui
Jeremy Hur-witz
Michael Acheson Isard
Abe Ittycheriah
Matthew Jagiel-ski
Wenhao Jia
Kathleen Kenealy
M. Krikun
Sneha Kudugunta 0001
Chang Lan
Kather-ine Lee
Benjamin Lee
Music Eric Li
Wei Li
YaGuang Li
Li Jian
Hyeontaek Li
Hanzhao Lim
Zhongtao Lin
Liu Frederick
Marcello Liu
Aroma Maggioni
Mahendru Joshua
Vedant Maynez
Maysam Misra
Moussalem Zachary
John Nado
E. Nham
Andrew Ni
Alicia Nys-trom
Marie Parrish
M. Pellat
Polacek Alex
Reiner Polozov
Siyuan Pope
Emily Qiao
Reif Bryan
Parker Richter
Alex Riley
Castro Ros
Aurko Roy
Brennan Saeta
Rajkumar Samuel
Renee Shelby
Ambrose Slone
Daniel Smilkov
David R. So
Daniel Sohn
Simon Tokumine
Dasha Valter
Haim-ing Bao
Mo Bavarian
Jeff Belgum
Ir-wan Bello
Jake Berdine
Gabriel Bernadett-Shapiro
Christopher Berner
Lenny Bogdonoff
Oleg Boiko
Madelaine Boyd
Anna-Luisa Brakman
Greg Brock-man
Tim Brooks
M. Brundage
Kevin Button
Trevor Cai
Rosie Campbell
Andrew Cann
Brittany Carey
Chelsea Carlson
Rory Carmichael
Brooke Chan
Che Chang
Fotis Chantzis
Derek Chen
Sully Chen
Ruby Chen
Jason Chen
Mark Chen
Benjamin Chess
Chester Cho
Hyung Casey Chu
Won Chung
Dave Cummings
Jeremiah Currier
Yunxing Dai
Tarun Goel
Gabriel Gogineni
Rapha Goh
Jonathan Gontijo-Lopes
Morgan Gordon
Scott Grafstein
Ryan Gray
Joshua Greene
Shixiang Shane Gross
Yufei Gu
Chris Guo
Jesse Hallacy
Jeff Han
Harris Yuchen
Mike He
Johannes Heaton
C. Heidecke
Alan Hesse
Wade Hickey
Peter Hickey
Hoeschele Brandon
Kenny Houghton
Shengli Hsu
Xin Hu
Joost Hu
Shantanu Huizinga
Shawn Jain
Jain Joanne
Angela Jang
Roger Jiang
Haozhun Jiang
Denny Jin
Shino Jin
Billie Jomoto
Hee-woo Jonn
Tomer Jun
Łukasz Kaftan
Ali Kaiser
Ingmar Ka-mali
Kanitscheider
Nitish Shirish
Keskar Tabarak
Logan Khan
J. Kilpatrick
Kim Christina
Yongjik Kim
Jan Hendrik Kim
Jamie Kirch-ner
Matt Kiros
Daniel Knight
Kokotajlo Łukasz
A. Kondraciuk
Aris Kondrich
Kyle Kon-stantinidis
Gretchen Kosic
Vishal Krueger
Michael Kuo
Ikai Lampe
Teddy Lan
Jan Lee
Jade Leike
Daniel Leung
Chak Ming Levy
Li Rachel
Molly Lim
Stephanie Lin
Mateusz Lin
Theresa Litwin
Ryan Lopez
Patricia Lowe
Lue Anna
Kim Makanju
S. Malfacini
Todor Manning
Yaniv Markov
Bianca Markovski
Katie Martin
Andrew Mayer
Bob Mayne
Scott Mayer McGrew
Christine McKinney
Paul McLeavey
McMillan Jake
David McNeil
Aalok Medina
Jacob Mehta
Luke Menick
Andrey Metz
Pamela Mishchenko
Vinnie Mishkin
Evan Monaco
Daniel Morikawa
Tong Mossing
Mira Mu
Oleg Murati
David Murk
Ashvin Mély
Reiichiro Nair
Rajeev Nakano
Nayak Arvind
Richard Neelakantan
Hyeonwoo Ngo
Noh Long
Cullen Ouyang
Jakub O’Keefe
Alex Pachocki
J. Paino
Ashley Palermo
Pantuliano
Carl Ross
Bob Rotsted
Henri Roussez
Nick Ry-der
Mario Saltarelli
Ted Sanders
Shibani Santurkar
Girish Sastry
Heather Schmidt
David Schnurr
John Schulman
Daniel Selsam
Kyla Sheppard
Toki Sherbakov
Jessica Shieh
Sarah Shoker
Pranav Shyam
Szymon Sidor
Eric Sigler
Maddie Simens
Jordan Sitkin
Katarina Slama
Ian Sohl
Benjamin D. Sokolowsky
Yang Song
Natalie Staudacher
Clemens Winter
Samuel Wolrich
Hannah Wong
Lauren Workman
Sherwin Wu
Michael Wu
Kai Xiao
Tao Xu
Sarah Yoo
Kevin Yu
Qim-ing Yuan
Wojciech Zaremba
Rowan G. Zellers
Chong Zhang
Marvin Zhang
Tianhao Shengjia Zhao
Ouyang Long
Jeff Wu
Xu Jiang
Diogo Almeida
C. Wainwright
Pamela Mishkin
Sandhini Agarwal
Alex Ray
Jacob Hilton
Fraser Kelton
Luke Miller
Amanda Askell
Peter Welinder
Paul F. Christiano
Jan Leike
Ryan Lowe. 2022
Adam Paszke
Sam Gross
Francisco Massa
Adam Lerer
Gregory Chanan
Trevor Killeen
Ze-Bin Lin
Natalia Gimelshein
L. Antiga
Alban Desmaison
Andreas Köpf
Edward Yang
Zachary DeVito
Martin Raison
A. Tejani
Sasank Chilamkurthy
Benoit Steiner
Giovanni Puccetti
Anna Rogers
Aleksandr Drozd
Felice
Dell’Orletta. 2022. Outlier
Alec Radford
Jong Wook Kim
Chris Hallacy
Aditya Ramesh
Gabriel Goh
Girish Sas-try
J. Clark
Rewon Child
David Luan
Victor Sanh
Alex Webson
Colin Raffel
Stephen H. Bach
Lintang A. Sutawika
Zaid Alyafeai
Antoine Chaffin
Arnaud Stiegler
Arun Raja
Manan Dey
Saiful Bari
Canwen Xu
Urmish Thakker
Shanya Sharma Sharma
Eliza Szczechla
Taewoon Kim 0002
Gunjan Chhablani
Ni-hal Nayak
Debajyoti Datta
Mike Jonathan Chang
Tian-Jian Jiang
Han Wang
Matteo Manica
Sheng Shen
Zheng-Xin Yong
Harshit Pandey
Rachel Bawden
Thomas Wang
Trishala Neeraj
Jos Rozen
Abheesht Sharma
Thibault Févry
Jason Alan Fries
Ryan Teehan
Teven Le Scao
Stella Biderman
Leo Gao
Thomas Wolf 0008
A. M. R. 2022
Multi-task
Richard Socher
Alex Perelygin
Jean Wu
Jason Chuang
Christopher D Manning
Andrew Ng
Christopher Potts
Recursive
Aarohi Srivastava
Abhinav Rastogi
Abhishek Rao
Abu Awal
Md. Shoeb
Abubakar Abid
Adam Fisch
Adam R. Brown
Adam Santoro
Aditya Gupta
Adrià Garriga-Alonso
Agnieszka Kluska
Aitor Lewkowycz
Akshat Agarwal
Alethea Power
Alex Warstadt
Alexander W. Kocurek
Ali Safaya
Ali Tazarv
Alice Xiang
Alicia Parrish
Allen Nie
Aman Hussain
Amanda Dsouza
Ameet Rahane
Anantharaman S. Iyer
Anders Johan Andreassen
Andrea Madotto
Andrea Santilli
Andreas Stuhlmüller
Andrew La
Andrew Lampinen
Andy Zou
Angela Jiang
Angelica Chen
Anh Vuong
Animesh Gupta
Anna Gottardi
Antonio Norelli
Anu Venkatesh
Arash Gholamidavoodi
Arfa Tabassum
Arul Menezes
Arun Kirubara-jan
Asher Mullokandov
Ashish Sabharwal
Austin Herrick
Avia Efrat
Aykut Erdem
Ayla Karaka¸s
Ryan Roberts
Bao Sheng Loe
Barret Zoph
Bartłomiej Bojanowski
Batuhan Özyurt
Behnam Hedayatnia
Behnam Neyshabur
Benjamin Inden
Benno Stein
Berk Ekmekci
Bill Yuchen
Blake Lin
Bryan Howald
Cameron Orinion
Cameron Diao
Catherine Dour
Cedrick Stinson
César Argueta
Chandan Ferri
Charles Singh
Chenlin Rathkopf
Chitta Meng
C. Baral
Chris Wu
Chris Callison-Burch
Christopher Waites
Christo-pher D Voigt
Cindy Potts
E. RamirezClara
Clemencia Rivera
Colin Siro
Court-ney Raffel
Cristina Ashcraft
Damien Garbacea
Sileo Dan
Dan Garrette
Dan Hendrycks
Dan Kilman
C. Roth
C. Daniel Freeman
Daniel Khashabi
Daniel Levy
Daniel Moseguí González
Danielle Perszyk
Danny Hernandez
Danqi Chen
2025-03-31
Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (published)
doi.org
Trade‐off of different deep learning‐based auto‐segmentation approaches for treatment planning of pediatric craniospinal irradiation autocontouring of OARs for pediatric CSI
Alana Thibodeau‐Antonacci
Marija Popovic
Ozgur Ates
Chia‐Ho Hua
James Schneider
Sonia Skamene
Carolyn Freeman
S. Enger
James Man Git Tsui
As auto‐segmentation tools become integral to radiotherapy, more commercial products emerge. However, they may not always suit our needs. … (see more)One notable example is the use of adult‐trained commercial software for the contouring of organs at risk (OARs) of pediatric patients.
2025-03-31
Medical Physics (Lancaster) (published)
doi.org
NoProp: Training Neural Networks without Back-propagation or Forward-propagation
Qinyu Li
Yee Whye Teh
Razvan Pascanu
2025-03-30
ArXiv (preprint)
doi.org
arxiv.org
Universal algorithm for transforming Hamiltonian eigenvalues
Tatsuki Odake
Hlér Kristjánsson
Philip Taranto
Mio Murao
Manipulating Hamiltonians governing physical systems has found a broad range of applications, from quantum chemistry to semiconductor design… (see more). In this work, we provide a new way of manipulating Hamiltonians, by transforming their eigenvalues while keeping their eigenstates unchanged. We develop a universal algorithm that deterministically implements any desired (suitably differentiable) function on the eigenvalues of any unknown Hamiltonian, whose positive-time and negative-time dynamics are given as a black box. Our algorithm uses correlated randomness to efficiently combine two subroutines -- namely controlization and Fourier series simulation -- exemplifying a general compilation procedure that we develop. The time complexity of our algorithm is significantly reduced via said compilation technique compared to a na{ï}ve concatenation of the subroutines and outperforms similar methods based on the quantum singular value transformation.
2025-03-30
Physical Review Research (published)
doi.org
arxiv.org
Steering CLIP's vision transformer with sparse autoencoders
Sonia Joseph
Praneet Suresh
Ethan Goldfarb
Lorenz Hufe
Yossi Gandelsman
Robert Graham
Danilo Bzdok
Wojciech Samek
Blake Aaron Richards
While vision models are highly capable, their internal mechanisms remain poorly understood-- a challenge which sparse autoencoders (SAEs) ha… (see more)ve helped address in language, but which remains underexplored in vision. We address this gap by training SAEs on CLIP's vision transformer and uncover key differences between vision and language processing, including distinct sparsity patterns for SAEs trained across layers and token types. We then provide the first systematic analysis of the steerability of CLIP's vision transformer by introducing metrics to quantify how precisely SAE features can be steered to affect the model's output. We find that 10-15% of neurons and features are steerable, with SAEs providing thousands more steerable features than the base model. Through targeted suppression of SAE features, we then demonstrate improved performance on three vision disentanglement tasks (CelebA, Waterbirds, and typographic attacks), finding optimal disentanglement in middle model layers, and achieving state-of-the-art performance on defense against typographic attacks. We release our CLIP SAE models and code to support future research in vision transformer interpretability.
2025-03-29
MIV @ IEEE/CVF Conference on Computer Vision and Pattern Recognition (poster)
doi.org
openreview.net
Bridging biodiversity and ecosystem services through useful plant species
Nina Obiar
Isaac Eckert
Janelle Baker
Daniel Moerman
Laura J. Pollock
2025-03-27
Plants, People, Planet (published)
doi.org
debug-gym: A Text-Based Environment for Interactive Debugging
Xingdi Yuan
Morgane M Moss
Charbel Feghali
Chinmay Singh
Darya Moldavskaya
Drew MacPhee
Lucas Caccia
Matheus Pereira
Minseon Kim
Alessandro Sordoni
Marc-Alexandre Côté
2025-03-26
ArXiv (preprint)
doi.org
arxiv.org
How do language models learn facts? Dynamics, curricula and hallucinations
Nicolas Zucchet
Jörg Bornschein
Stephanie Chan
Andrew Lampinen
Razvan Pascanu
Soham De
2025-03-26
ArXiv (preprint)
doi.org
arxiv.org
PRISM: High-Resolution & Precise Counterfactual Medical Image Generation using Language-guided Stable Diffusion
Amar Kumar
Anita Kriz
Mohammad Havaei
Tal Arbel
Developing reliable and generalizable deep learning systems for medical imaging faces significant obstacles due to spurious correlations, da… (see more)ta imbalances, and limited text annotations in datasets. Addressing these challenges requires architectures robust to the unique complexities posed by medical imaging data. The rapid advancements in vision-language foundation models within the natural image domain prompt the question of how they can be adapted for medical imaging tasks. In this work, we present PRISM, a framework that leverages foundation models to generate high-resolution, language-guided medical image counterfactuals using Stable Diffusion. Our approach demonstrates unprecedented precision in selectively modifying spurious correlations (the medical devices) and disease features, enabling the removal and addition of specific attributes while preserving other image characteristics. Through extensive evaluation, we show how PRISM advances counterfactual generation and enables the development of more robust downstream classifiers for clinically deployable solutions. To facilitate broader adoption and research, we make our code publicly available at https://github.com/Amarkr1/PRISM.
2025-03-26
MIDL.io/2025/Conference (oral)
doi.org
openreview.net
StarFlow: Generating Structured Workflow Outputs From Sketch Images
Patrice Béchard
Chao Wang
Amirhossein Abaskohi
Juan A. Rodriguez
Christopher Pal
David Vázquez
Spandana Gella
Sai Rajeswar
Perouz Taslakian
2025-03-26
ArXiv (preprint)
doi.org
arxiv.org
A Text-guided Protein Design Framework
Shengchao Liu
Yutao Zhu
Yanjing Li
Zhuoxinran Li
Jiarui Lu
Zhao Xu
Weili Nie
Anthony Gitter
Chaowei Xiao
Jian Tang
Arvind Ramanathan
Hongyu Guo
Anima Anandkumar
Current AI-assisted protein design mainly utilizes protein sequential and structural information. Meanwhile, there exists tremendous knowled… (see more)ge curated by humans in the text format describing proteins' high-level functionalities. Yet, whether the incorporation of such text data can help protein design tasks has not been explored. To bridge this gap, we propose ProteinDT, a multi-modal framework that leverages textual descriptions for protein design. ProteinDT consists of three subsequent steps: ProteinCLAP which aligns the representation of two modalities, a facilitator that generates the protein representation from the text modality, and a decoder that creates the protein sequences from the representation. To train ProteinDT, we construct a large dataset, SwissProtCLAP, with 441K text and protein pairs. We quantitatively verify the effectiveness of ProteinDT on three challenging tasks: (1) over 90% accuracy for text-guided protein generation; (2) best hit ratio on 12 zero-shot text-guided protein editing tasks; (3) superior performance on four out of six protein property prediction benchmarks.
2025-03-26
Nature Machine Intelligence (published)
doi.org
arxiv.org