Training Deep Networks With Synthetic Data: Bridging the Reality Gap by Domain Randomization. - Jonathan Tremblay; Aayush Prakash; David Acuna; Mark Brophy; Varun Jampani; Cem Anil; Thang To; Eric Cameracci; Shaad Boochoon; Stan Birchfield (CVPR Workshops, 2018)

TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer. - Sicong Huang 001; Qiyang Li; Cem Anil; Xuchan Bao; Sageev Oore; Roger B. Grosse (ICLR (Poster), 2019)

Sorting Out Lipschitz Function Approximation. - Cem Anil; James Lucas; Roger B. Grosse (ICML, 2019)

Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks. - Qiyang Li; Saminul Haque; Cem Anil; James Lucas; Roger B. Grosse; Jörn-Henrik Jacobsen (NeurIPS, 2019)

Learning to Elect. - Cem Anil; Xuchan Bao (NeurIPS, 2021)

Learning to Give Checkable Answers with Prover-Verifier Games. - Cem Anil; Guodong Zhang 006; Yuhuai Wu; Roger B. Grosse (CoRR, 2021)

Sparse capsule networks for informative representation learning in digital pathology. - Matthew McNeil; Cem Anil; Anne L. Martel (Digital and Computational Pathology, 2022)

Path Independent Equilibrium Models Can Better Exploit Test-Time Computation. - Cem Anil; Ashwini Pokle; Kaiqu Liang; Johannes Treutlein; Yuhuai Wu; Shaojie Bai; J. Zico Kolter; Roger B. Grosse (NeurIPS, 2022)

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)

Studying Large Language Model Generalization with Influence Functions. - Roger B. Grosse; Juhan Bae; Cem Anil; Nelson Elhage; Alex Tamkin; Amirhossein Tajdini; Benoit Steiner; Dustin Li; Esin Durmus; Ethan Perez; Evan Hubinger; Kamile Lukosiute; et al. (CoRR, 2023)

Many-shot Jailbreaking. - Cem Anil; Esin Durmus; Nina Panickssery; Mrinank Sharma; Joe Benton; Sandipan Kundu; Joshua Batson; Meg Tong; Jesse Mu; Daniel Ford; Francesco Mosconi; Rajashree Agrawal; et al. (NeurIPS, 2024)

Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data. - Johannes Treutlein; Dami Choi; Jan Betley; Samuel Marks; Cem Anil; Roger B. Grosse; Owain Evans (NeurIPS, 2024)

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training. - Evan Hubinger; Carson Denison; Jesse Mu; Mike Lambert; Meg Tong; Monte MacDiarmid; Tamera Lanham; Daniel M. Ziegler; Tim Maxwell; Newton Cheng; Adam S. Jermyn; Amanda Askell; et al. (CoRR, 2024)

Sabotage Evaluations for Frontier Models. - Joe Benton; Misha Wagner; Eric Christiansen; Cem Anil; Ethan Perez; Jai Srivastav; Esin Durmus; Deep Ganguli; Shauna Kravec; Buck Shlegeris; Jared Kaplan; Holden Karnofsky; et al. (CoRR, 2024)

Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming. - Mrinank Sharma; Meg Tong; Jesse Mu; Jerry Wei; Jorrit Kruthoff; Scott Goodfriend; Euan Ong; Alwin Peng; Raj Agarwal; Cem Anil; Amanda Askell; Nathan Bailey; et al. (CoRR, 2025)