Distilling a Neural Network Into a Soft Decision Tree. - Nicholas Frosst; Geoffrey E. Hinton (CEx@AI*IA, 2017)

Dynamic Routing Between Capsules. - Sara Sabour; Nicholas Frosst; Geoffrey E. Hinton (NIPS, 2017)

Matrix capsules with EM routing. - Geoffrey E. Hinton; Sara Sabour; Nicholas Frosst (ICLR (Poster), 2018)

DARCCC: Detecting Adversaries by Reconstruction from Class Conditional Capsules. - Nicholas Frosst; Sara Sabour; Geoffrey E. Hinton (CoRR, 2018)

SMILER: Saliency Model Implementation Library for Experimental Research. - Calden Wloka; Toni Kunic; Iuliia Kotseruba; Ramin Fahimi; Nicholas Frosst; Neil D. B. Bruce; John K. Tsotsos (CoRR, 2018)

Analyzing and Improving Representations with the Soft Nearest Neighbor Loss. - Nicholas Frosst; Nicolas Papernot; Geoffrey E. Hinton (ICML, 2019)

Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions. - Yao Qin 001; Nicholas Frosst; Sara Sabour; Colin Raffel; Garrison W. Cottrell; Geoffrey E. Hinton (ICLR, 2020)

Predicting Twitter Engagement With Deep Language Models. - Maksims Volkovs; Zhaoyue Cheng; Mathieu Ravaut; Hojin Yang; Kevin Shen; Jin Peng Zhou; Anson Wong; Saba Zuberi; Ivan Zhang; Nick Frosst; Helen Ngo; Carol Chen; et al. (RecSys Challenge, 2020)

Deflecting Adversarial Attacks. - Yao Qin 001; Nicholas Frosst; Colin Raffel; Garrison W. Cottrell; Geoffrey E. Hinton (CoRR, 2020)

Neural Additive Models: Interpretable Machine Learning with Neural Nets. - Rishabh Agarwal; Nicholas Frosst; Xuezhou Zhang; Rich Caruana; Geoffrey E. Hinton (CoRR, 2020)

Interlocking Backpropagation: Improving depthwise model-parallelism. - Aidan N. Gomez; Oscar Key; Stephen Gou; Nick Frosst; Jeff Dean; Yarin Gal (CoRR, 2020)

Neural Additive Models: Interpretable Machine Learning with Neural Nets. - Rishabh Agarwal; Levi Melnick; Nicholas Frosst; Xuezhou Zhang; Benjamin J. Lengerich; Rich Caruana; Geoffrey E. Hinton (NeurIPS, 2021)

Mitigating harm in language models with conditional-likelihood filtration. - Helen Ngo; Cooper Raterink; João G. M. Araújo; Ivan Zhang; Carol Chen; Adrien Morisot; Nicholas Frosst (CoRR, 2021)

No News is Good News: A Critique of the One Billion Word Benchmark. - Helen Ngo; João G. M. Araújo; Jeffrey Hui; Nicholas Frosst (CoRR, 2021)

Interlocking Backpropagation: Improving depthwise model-parallelism. - Aidan N. Gomez; Oscar Key; Kuba Perlin; Stephen Gou; Nick Frosst; Jeff Dean; Yarin Gal (J. Mach. Learn. Res., 2022)

Aya 23: Open Weight Releases to Further Multilingual Progress. - Viraat Aryabumi; John Dang; Dwarak Talupuru; Saurabh Dash; David Cairuz; Hangyu Lin; Bharat Venkitesh; Madeline Smith; Jon Ander Campos; Yi Chern Tan; Kelly Marchisio; Max Bartolo; et al. (CoRR, 2024)

Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier. - John Dang; Shivalika Singh; Daniel D'souza; Arash Ahmadian; Alejandro Salamanca; Madeline Smith; Aidan Peppin; Sungjin Hong; Manoj Govindassamy; Terrence Zhao; Sandra Kublik; Meor Amer; et al. (CoRR, 2024)

The Reality of AI and Biorisk. - Aidan Peppin; Anka Reuel; Stephen Casper; Elliot Jones; Andrew Strait; Usman Anwar; Anurag Agrawal; Sayash Kapoor; Sanmi Koyejo; Marie Pellat; Rishi Bommasani; Nick Frosst; et al. (FAccT, 2025)

Command A: An Enterprise-Ready Large Language Model. - Aakanksha; Arash Ahmadian; Marwan Ahmed; Jay Alammar; Milad Alizadeh; Yazeed Alnumay; Sophia Althammer; Arkady Arkhangorodsky; Viraat Aryabumi; Dennis Aumiller; Raphaël Avalos; Zahara Aviv; et al. (CoRR, 2025)

Aya Vision: Advancing the Frontier of Multilingual Multimodality. - Saurabh Dash; Yiyang Nan; John Dang; Arash Ahmadian; Shivalika Singh; Madeline Smith; Bharat Venkitesh; Vlad Shmyhlo; Viraat Aryabumi; Walter Beller-Morales; Jeremy Pekmez; Jason Ozuzu; et al. (CoRR, 2025)