Asymptotics of Wide Convolutional Neural Networks. - Anders Andreassen; Ethan Dyer (CoRR, 2020)

Understanding the failure modes of out-of-distribution generalization. - Vaishnavh Nagarajan; Anders Andreassen; Behnam Neyshabur (ICLR, 2021)

Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution. - Anders Andreassen; Patrick T. Komiske; Eric M. Metodiev; Benjamin Nachman; Adi Suresh; Jesse Thaler (CoRR, 2021)

Show Your Work: Scratchpads for Intermediate Computation with Language Models. - Maxwell I. Nye; Anders Johan Andreassen; Guy Gur-Ari; Henryk Michalewski; Jacob Austin; David Bieber; David Dohan; Aitor Lewkowycz; Maarten Bosma; David Luan; Charles Sutton; Augustus Odena (CoRR, 2021)

Exploring Length Generalization in Large Language Models. - Cem Anil; Yuhuai Wu; Anders Andreassen; Aitor Lewkowycz; Vedant Misra; Vinay V. Ramasesh; Ambrose Slone; Guy Gur-Ari; Ethan Dyer; Behnam Neyshabur (NeurIPS, 2022)

Solving Quantitative Reasoning Problems with Language Models. - Aitor Lewkowycz; Anders Andreassen; David Dohan; Ethan Dyer; Henryk Michalewski; Vinay V. Ramasesh; Ambrose Slone; Cem Anil; Imanol Schlag; Theo Gutman-Solo; Yuhuai Wu; Behnam Neyshabur; et al. (NeurIPS, 2022)

The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning. - Anders Johan Andreassen; Yasaman Bahri; Behnam Neyshabur; Rebecca Roelofs (Trans. Mach. Learn. Res., 2022)

Gemini: A Family of Highly Capable Multimodal Models. - Rohan Anil; Sebastian Borgeaud; Yonghui Wu; Jean-Baptiste Alayrac; Jiahui Yu; Radu Soricut; Johan Schalkwyk; Andrew M. Dai; Anja Hauth; Katie Millican; David Silver; Slav Petrov; et al. (CoRR, 2023)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - 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; et al. (Trans. Mach. Learn. Res., 2023)