Jonathan Frankle

63 papers A* 17Journal 41Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	KARL: Knowledge Agents via Reinforcement Learning. CoRR Jonathan D. Chang, Andrew Drozdov, Shubham Toshniwal, Owen Oertell, Alexander Trott, Jacob P. Portes, Abhay Gupta, Pallavi Koppol, Ashutosh Baheti, Sean Kulinski, Ivan Zhou, Irene Dea, Krista Opsahl-Ong, Simon Favreau-Lessard, Sean Owen, Jose Javier Gonzalez Ortiz, Arnav Singhvi, Xabi Andrade, Cindy Wang, Kartik Sreenivasan, Sam Havens, Jialu Liu, Peyton DeNiro, Wen Sun, Michael Bendersky, Jonathan Frankle
2025	J	`jnl`	A State-of-the-Art SQL Reasoning Model using RLVR. CoRR Alnur Ali, Ashutosh Baheti, Jonathan Chang, Ta-Chung Chi, Brandon Cui, Andrew Drozdov, Jonathan Frankle, Abhay Gupta, Pallavi Koppol, Sean Kulinski, Jonathan Li, Dipendra Misra, Krista Opsahl-Ong, Jose Javier Gonzalez Ortiz, Matei Zaharia, Yue Zhang
2025	J	`jnl`	Soup to go: mitigating forgetting during continual learning with model averaging. CoRR Anat Kleiman, Gintare Karolina Dziugaite, Jonathan Frankle, Sham M. Kakade, Mansheej Paul
2024	A*	`conf`	Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws. ICML Nikhil Sardana, Jacob P. Portes, Sasha Doubov, Jonathan Frankle
2024	J	`jnl`	Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws. CoRR Nikhil Sardana, Jonathan Frankle
2024	J	`jnl`	BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text. CoRR Elliot Bolton, Abhinav Venigalla, Michihiro Yasunaga, David Hall, Betty Xiong, Tony Lee, Roxana Daneshjou, Jonathan Frankle, Percy Liang, Michael Carbin, Christopher D. Manning
2024	A*	`conf`	Common Canvas: Open Diffusion Models Trained on Creative-Commons Images. CVPR Aaron Gokaslan, A. Feder Cooper, Jasmine Collins, Landan Seguin, Austin Jacobson, Mihir Patel, Jonathan Frankle, Cory Stephenson, Volodymyr Kuleshov
2024	J	`jnl`	Dataset Difficulty and the Role of Inductive Bias. CoRR Devin Kwok, Nikhil Anand, Jonathan Frankle, Gintare Karolina Dziugaite, David Rolnick
2024	J	`jnl`	Does your data spark joy? Performance gains from domain upsampling at the end of training. CoRR Cody Blakeney, Mansheej Paul, Brett W. Larsen, Sean Owen, Jonathan Frankle
2024	—	`conf`	Dynamic Masking Rate Schedules for MLM Pretraining. EACL (2) Zachary Ankner, Naomi Saphra, Davis W. Blalock, Jonathan Frankle, Matthew L. Leavitt
2024	J	`jnl`	LoRA Learns Less and Forgets Less. CoRR Dan Biderman, Jose Javier Gonzalez Ortiz, Jacob P. Portes, Mansheej Paul, Philip Greengard, Connor Jennings, Daniel King, Sam Havens, Vitaliy Chiley, Jonathan Frankle, Cody Blakeney, John P. Cunningham
2024	J	`jnl`	LoRA Learns Less and Forgets Less. Trans. Mach. Learn. Res. 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
2023	J	`jnl`	CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images. CoRR Aaron Gokaslan, A. Feder Cooper, Jasmine Collins, Landan Seguin, Austin Jacobson, Mihir Patel, Jonathan Frankle, Cory Stephenson, Volodymyr Kuleshov
2023	J	`jnl`	Dynamic Masking Rate Schedules for MLM Pretraining. CoRR Zachary Ankner, Naomi Saphra, Davis W. Blalock, Jonathan Frankle, Matthew L. Leavitt
2023	J	`jnl`	Knowledge Distillation for Efficient Sequences of Training Runs. CoRR Xingyu Liu, Alex Leonardi, Lu Yu, Chris Gilmer-Hill, Matthew L. Leavitt, Jonathan Frankle
2023	A*	`conf`	MosaicBERT: A Bidirectional Encoder Optimized for Fast Pretraining. NeurIPS Jacob P. Portes, Alexander Trott, Sam Havens, Daniel King, Abhinav Venigalla, Moin Nadeem, Nikhil Sardana, Daya Khudia, Jonathan Frankle
2023	J	`jnl`	MosaicBERT: A Bidirectional Encoder Optimized for Fast Pretraining. CoRR Jacob P. Portes, Alex Trott, Sam Havens, Daniel King, Abhinav Venigalla, Moin Nadeem, Nikhil Sardana, Daya Khudia, Jonathan Frankle
2023	J	`jnl`	Report of the 1st Workshop on Generative AI and Law. CoRR 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, Christopher De Sa, Jonathan Frankle, Deep Ganguli, Bryant Gipson, Andres Guadamuz, Swee Leng Harris, Abigail Z. Jacobs, Elizabeth Joh, Gautam Kamath, Mark A. Lemley, Cass Matthews, Christine McLeavey, Corynne McSherry, Milad Nasr, Paul Ohm, Adam Roberts, Tom Rubin, Pamela Samuelson, Ludwig Schubert, Kristen Vaccaro, Luis Villa, Felix Wu, Elana Zeide
2023	—	`—`	The Lottery Ticket Hypothesis: On Sparse, Trainable Neural Networks Jonathan Frankle
2023	A*	`conf`	Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask? ICLR Mansheej Paul, Feng Chen, Brett W. Larsen, Jonathan Frankle, Surya Ganguli, Gintare Karolina Dziugaite
2022	J	`jnl`	Fast Benchmarking of Accuracy vs. Training Time with Cyclic Learning Rates. CoRR Jacob P. Portes, Davis W. Blalock, Cory Stephenson, Jonathan Frankle
2022	A*	`conf`	Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks. NeurIPS Mansheej Paul, Brett W. Larsen, Surya Ganguli, Jonathan Frankle, Gintare Karolina Dziugaite
2022	J	`jnl`	Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks. CoRR Mansheej Paul, Brett W. Larsen, Surya Ganguli, Jonathan Frankle, Gintare Karolina Dziugaite
2022	J	`jnl`	Non-Determinism and the Lawlessness of ML Code. CoRR A. Feder Cooper, Jonathan Frankle, Christopher De Sa
2022	—	`conf`	Non-Determinism and the Lawlessness of Machine Learning Code. CSLAW A. Feder Cooper, Jonathan Frankle, Christopher De Sa
2022	A*	`conf`	Pruning's Effect on Generalization Through the Lens of Training and Regularization. NeurIPS Tian Jin, Michael Carbin, Daniel M. Roy, Jonathan Frankle, Gintare Karolina Dziugaite
2022	J	`jnl`	Pruning's Effect on Generalization Through the Lens of Training and Regularization. CoRR Tian Jin, Michael Carbin, Daniel M. Roy, Jonathan Frankle, Gintare Karolina Dziugaite
2022	J	`jnl`	Reduce, Reuse, Recycle: Improving Training Efficiency with Distillation. CoRR Cody Blakeney, Jessica Zosa Forde, Jonathan Frankle, Ziliang Zong, Matthew L. Leavitt
2022	J	`jnl`	Strengthening Subcommunities: Towards Sustainable Growth in AI Research. CoRR Andi Peng, Jessica Zosa Forde, Yonadav Shavit, Jonathan Frankle
2022	J	`jnl`	The Effect of Data Dimensionality on Neural Network Prunability. CoRR Zachary Ankner, Alex Renda, Gintare Karolina Dziugaite, Jonathan Frankle, Tian Jin
2022	J	`jnl`	Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask? CoRR Mansheej Paul, Feng Chen, Brett W. Larsen, Jonathan Frankle, Surya Ganguli, Gintare Karolina Dziugaite
2022	A*	`conf`	What Can Linear Interpolation of Neural Network Loss Landscapes Tell Us? ICML Tiffany J. Vlaar, Jonathan Frankle
2021	A*	`conf`	On the Predictability of Pruning Across Scales. ICML Jonathan S. Rosenfeld, Jonathan Frankle, Michael Carbin, Nir Shavit
2021	A*	`conf`	Pruning Neural Networks at Initialization: Why Are We Missing the Mark? ICLR Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
2021	J	`jnl`	Studying the Consistency and Composability of Lottery Ticket Pruning Masks. CoRR Rajiv Movva, Jonathan Frankle, Michael Carbin
2021	A*	`conf`	The Lottery Tickets Hypothesis for Supervised and Self-Supervised Pre-Training in Computer Vision Models. CVPR Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Michael Carbin, Zhangyang Wang
2021	J	`jnl`	Trade-offs of Local SGD at Scale: An Empirical Study. CoRR Jose Javier Gonzalez Ortiz, Jonathan Frankle, Mike Rabbat, Ari S. Morcos, Nicolas Ballas
2021	A*	`conf`	Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs. ICLR Jonathan Frankle, David J. Schwab, Ari S. Morcos
2021	J	`jnl`	What can linear interpolation of neural network loss landscapes tell us? CoRR Tiffany Vlaar, Jonathan Frankle
2020	J	`jnl`	Are all negatives created equal in contrastive instance discrimination? CoRR Tiffany Tianhui Cai, Jonathan Frankle, David J. Schwab, Ari S. Morcos
2020	A*	`conf`	Comparing Rewinding and Fine-tuning in Neural Network Pruning. ICLR Alex Renda, Jonathan Frankle, Michael Carbin
2020	J	`jnl`	Comparing Rewinding and Fine-tuning in Neural Network Pruning. CoRR Alex Renda, Jonathan Frankle, Michael Carbin
2020	A*	`conf`	Linear Mode Connectivity and the Lottery Ticket Hypothesis. ICML Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
2020	J	`jnl`	On the Predictability of Pruning Across Scales. CoRR Jonathan S. Rosenfeld, Jonathan Frankle, Michael Carbin, Nir Shavit
2020	J	`jnl`	Pruning Neural Networks at Initialization: Why are We Missing the Mark? CoRR Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
2020	J	`jnl`	Revisiting "Qualitatively Characterizing Neural Network Optimization Problems". CoRR Jonathan Frankle
2020	A*	`conf`	The Early Phase of Neural Network Training. ICLR Jonathan Frankle, David J. Schwab, Ari S. Morcos
2020	J	`jnl`	The Early Phase of Neural Network Training. CoRR Jonathan Frankle, David J. Schwab, Ari S. Morcos
2020	A*	`conf`	The Lottery Ticket Hypothesis for Pre-trained BERT Networks. NeurIPS Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, Michael Carbin
2020	J	`jnl`	The Lottery Ticket Hypothesis for Pre-trained BERT Networks. CoRR Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, Michael Carbin
2020	J	`jnl`	The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models. CoRR Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Michael Carbin, Zhangyang Wang
2020	J	`jnl`	Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs. CoRR Jonathan Frankle, David J. Schwab, Ari S. Morcos
2020	—	`conf`	What is the State of Neural Network Pruning? MLSys Davis W. Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, John V. Guttag
2020	J	`jnl`	What is the State of Neural Network Pruning? CoRR Davis W. Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, John V. Guttag
2019	J	`jnl`	Dissecting Pruned Neural Networks. CoRR Jonathan Frankle, David Bau
2019	J	`jnl`	Linear Mode Connectivity and the Lottery Ticket Hypothesis. CoRR Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
2019	J	`jnl`	The Lottery Ticket Hypothesis at Scale. CoRR Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
2019	—	`conf`	The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks. ICLR Jonathan Frankle, Michael Carbin
2018	A*	`conf`	Practical Accountability of Secret Processes. USENIX Security Symposium Jonathan Frankle, Sunoo Park, Daniel Shaar, Shafi Goldwasser, Daniel J. Weitzner
2018	J	`jnl`	Practical Accountability of Secret Processes. IACR Cryptol. ePrint Arch. Jonathan Frankle, Sunoo Park, Daniel Shaar, Shafi Goldwasser, Daniel J. Weitzner
2018	J	`jnl`	The Lottery Ticket Hypothesis: Training Pruned Neural Networks. CoRR Jonathan Frankle, Michael Carbin
2016	A*	`conf`	Example-directed synthesis: a type-theoretic interpretation. POPL Jonathan Frankle, Peter-Michael Osera, David Walker, Steve Zdancewic
2015	J	`jnl`	Type-Directed Synthesis of Products. CoRR Jonathan Frankle