Type-Directed Synthesis of Products. - Jonathan Frankle (CoRR, 2015)

Example-directed synthesis: a type-theoretic interpretation. - Jonathan Frankle; Peter-Michael Osera; David Walker 001; Steve Zdancewic (POPL, 2016)

The Lottery Ticket Hypothesis: Training Pruned Neural Networks. - Jonathan Frankle; Michael Carbin (CoRR, 2018)

Practical Accountability of Secret Processes. - Jonathan Frankle; Sunoo Park; Daniel Shaar; Shafi Goldwasser; Daniel J. Weitzner (IACR Cryptol. ePrint Arch., 2018)

The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks. - Jonathan Frankle; Michael Carbin (ICLR, 2019)

The Lottery Ticket Hypothesis at Scale. - Jonathan Frankle; Gintare Karolina Dziugaite; Daniel M. Roy 001; Michael Carbin (CoRR, 2019)

Dissecting Pruned Neural Networks. - Jonathan Frankle; David Bau (CoRR, 2019)

The Early Phase of Neural Network Training. - Jonathan Frankle; David J. Schwab; Ari S. Morcos (ICLR, 2020)

Comparing Rewinding and Fine-tuning in Neural Network Pruning. - Alex Renda; Jonathan Frankle; Michael Carbin (ICLR, 2020)

Linear Mode Connectivity and the Lottery Ticket Hypothesis. - Jonathan Frankle; Gintare Karolina Dziugaite; Daniel M. Roy 001; Michael Carbin (ICML, 2020)

What is the State of Neural Network Pruning? - Davis W. Blalock; Jose Javier Gonzalez Ortiz; Jonathan Frankle; John V. Guttag (MLSys, 2020)

The Lottery Ticket Hypothesis for Pre-trained BERT Networks. - Tianlong Chen 001; Jonathan Frankle; Shiyu Chang; Sijia Liu 001; Yang Zhang 001; Zhangyang Wang; Michael Carbin (NeurIPS, 2020)

TIRAMISU: A Polyhedral Compiler for Dense and Sparse Deep Learning. - Riyadh Baghdadi; Abdelkader Nadir Debbagh; Kamel Abdous; Fatima-Zohra Benhamida; Alex Renda; Jonathan Elliott Frankle; Michael Carbin; Saman P. Amarasinghe (CoRR, 2020)

Are all negatives created equal in contrastive instance discrimination? - Tiffany Tianhui Cai; Jonathan Frankle; David J. Schwab; Ari S. Morcos (CoRR, 2020)

Revisiting "Qualitatively Characterizing Neural Network Optimization Problems". - Jonathan Frankle (CoRR, 2020)

The Lottery Tickets Hypothesis for Supervised and Self-Supervised Pre-Training in Computer Vision Models. - Tianlong Chen 001; Jonathan Frankle; Shiyu Chang; Sijia Liu 001; Yang Zhang 001; Michael Carbin; Zhangyang Wang (CVPR, 2021)

Pruning Neural Networks at Initialization: Why Are We Missing the Mark? - Jonathan Frankle; Gintare Karolina Dziugaite; Daniel M. Roy 001; Michael Carbin (ICLR, 2021)

Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs. - Jonathan Frankle; David J. Schwab; Ari S. Morcos (ICLR, 2021)

On the Predictability of Pruning Across Scales. - Jonathan S. Rosenfeld; Jonathan Frankle; Michael Carbin; Nir Shavit (ICML, 2021)

Studying the Consistency and Composability of Lottery Ticket Pruning Masks. - Rajiv Movva; Jonathan Frankle; Michael Carbin (CoRR, 2021)

Trade-offs of Local SGD at Scale: An Empirical Study. - Jose Javier Gonzalez Ortiz; Jonathan Frankle; Mike Rabbat; Ari S. Morcos; Nicolas Ballas (CoRR, 2021)

Non-Determinism and the Lawlessness of Machine Learning Code. - A. Feder Cooper; Jonathan Frankle; Christopher De Sa (CSLAW, 2022)

What Can Linear Interpolation of Neural Network Loss Landscapes Tell Us? - Tiffany J. Vlaar; Jonathan Frankle (ICML, 2022)

Pruning's Effect on Generalization Through the Lens of Training and Regularization. - Tian Jin; Michael Carbin; Daniel M. Roy 001; Jonathan Frankle; Gintare Karolina Dziugaite (NeurIPS, 2022)

Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks. - Mansheej Paul; Brett W. Larsen; Surya Ganguli; Jonathan Frankle; Gintare Karolina Dziugaite (NeurIPS, 2022)

Strengthening Subcommunities: Towards Sustainable Growth in AI Research. - Andi Peng; Jessica Zosa Forde; Yonadav Shavit; Jonathan Frankle (CoRR, 2022)

Fast Benchmarking of Accuracy vs. Training Time with Cyclic Learning Rates. - Jacob P. Portes; Davis W. Blalock; Cory Stephenson; Jonathan Frankle (CoRR, 2022)

Non-Determinism and the Lawlessness of ML Code. - A. Feder Cooper; Jonathan Frankle; Christopher De Sa (CoRR, 2022)

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

The Effect of Data Dimensionality on Neural Network Prunability. - Zachary Ankner; Alex Renda; Gintare Karolina Dziugaite; Jonathan Frankle; Tian Jin (CoRR, 2022)

Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask? - Mansheej Paul; Feng Chen 046; Brett W. Larsen; Jonathan Frankle; Surya Ganguli; Gintare Karolina Dziugaite (ICLR, 2023)

MosaicBERT: A Bidirectional Encoder Optimized for Fast Pretraining. - Jacob P. Portes; Alexander Trott; Sam Havens; Daniel King; Abhinav Venigalla; Moin Nadeem; Nikhil Sardana; Daya Khudia; Jonathan Frankle (NeurIPS, 2023)

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

CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images. - Aaron Gokaslan; A. Feder Cooper; Jasmine Collins; Landan Seguin; Austin Jacobson; Mihir Patel; Jonathan Frankle; Cory Stephenson; Volodymyr Kuleshov (CoRR, 2023)

Report of the 1st Workshop on Generative AI and Law. - A. Feder Cooper; Katherine Lee; James Grimmelmann; Daphne Ippolito; Christopher Callison-Burch; Christopher A. Choquette-Choo; Niloofar Mireshghallah; Miles Brundage; David Mimno; Madiha Zahrah Choksi; Jack M. Balkin; Nicholas Carlini; et al. (CoRR, 2023)

The Lottery Ticket Hypothesis: On Sparse, Trainable Neural Networks - Jonathan Frankle (2023)

Common Canvas: Open Diffusion Models Trained on Creative-Commons Images. - Aaron Gokaslan; A. Feder Cooper; Jasmine Collins; Landan Seguin; Austin Jacobson; Mihir Patel; Jonathan Frankle; Cory Stephenson; Volodymyr Kuleshov (CVPR, 2024)

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

Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws. - Nikhil Sardana; Jacob P. Portes; Sasha Doubov; Jonathan Frankle (ICML, 2024)

Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws. - Nikhil Sardana; Jonathan Frankle (CoRR, 2024)

Dataset Difficulty and the Role of Inductive Bias. - Devin Kwok; Nikhil Anand; Jonathan Frankle; Gintare Karolina Dziugaite; David Rolnick (CoRR, 2024)

BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text. - Elliot Bolton; Abhinav Venigalla; Michihiro Yasunaga; David Hall 006; Betty Xiong; Tony Lee; Roxana Daneshjou; Jonathan Frankle; Percy Liang; Michael Carbin; Christopher D. Manning (CoRR, 2024)

Does your data spark joy? Performance gains from domain upsampling at the end of training. - Cody Blakeney; Mansheej Paul; Brett W. Larsen; Sean Owen; Jonathan Frankle (CoRR, 2024)

LoRA Learns Less and Forgets Less. - Dan Biderman; Jacob P. Portes; Jose Javier Gonzalez Ortiz; Mansheej Paul; Philip Greengard; Connor Jennings; Daniel King; Sam Havens; Vitaliy Chiley; Jonathan Frankle; Cody Blakeney; John Patrick Cunningham (Trans. Mach. Learn. Res., 2024)

Soup to go: mitigating forgetting during continual learning with model averaging. - Anat Kleiman; Gintare Karolina Dziugaite; Jonathan Frankle; Sham M. Kakade; Mansheej Paul (CoRR, 2025)