On the relationship between class selectivity, dimensionality, and robustness. - Matthew L. Leavitt; Ari S. Morcos (CoRR, 2020)

Linking average- and worst-case perturbation robustness via class selectivity and dimensionality. - Matthew L. Leavitt; Ari S. Morcos (CoRR, 2020)

Towards falsifiable interpretability research. - Matthew L. Leavitt; Ari S. Morcos (CoRR, 2020)

Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs. - Matthew L. Leavitt; Ari S. Morcos (ICLR, 2021)

ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases. - Stéphane d'Ascoli; Hugo Touvron; Matthew L. Leavitt; Ari S. Morcos; Giulio Biroli; Levent Sagun (ICML, 2021)

Reduce, Reuse, Recycle: Improving Training Efficiency with Distillation. - Cody Blakeney; Jessica Zosa Forde; Jonathan Frankle; Ziliang Zong; Matthew L. Leavitt (CoRR, 2022)

Knowledge Distillation for Efficient Sequences of Training Runs. - Xingyu Liu; Alex Leonardi; Lu Yu; Chris Gilmer-Hill; Matthew L. Leavitt; Jonathan Frankle (CoRR, 2023)

On the special role of class-selective neurons in early training. - Omkar Ranadive; Nikhil Thakurdesai; Ari S. Morcos; Matthew L. Leavitt; Stéphane Deny (Trans. Mach. Learn. Res., 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)

Dynamic Masking Rate Schedules for MLM Pretraining. - Zachary Ankner; Naomi Saphra; Davis W. Blalock; Jonathan Frankle; Matthew L. Leavitt (EACL (2), 2024)

Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs. - Angelica Chen; Ravid Shwartz-Ziv; Kyunghyun Cho; Matthew L. Leavitt; Naomi Saphra (ICLR, 2024)

Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models. - Zachary Ankner; Cody Blakeney; Kartik Sreenivasan; Max Marion; Matthew L. Leavitt; Mansheej Paul (ICLR, 2025)

BeyondWeb: Lessons from Scaling Synthetic Data for Trillion-scale Pretraining. - Pratyush Maini; Vineeth Dorna; Parth Doshi; Aldo Carranza; Fan Pan; Jack Urbanek; Paul Burstein; Alex Fang; Alvin Deng; Amro Abbas; Brett W. Larsen; Cody Blakeney; et al. (CoRR, 2025)