Jonathan Frankle

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Michael CarbinMassachusetts Institute of TechnologyVerified email at csail.mit.edu
Gintare Karolina DziugaiteGoogle DeepMindVerified email at google.com
Ari S. MorcosResearch Scientist, Meta AI ResearchVerified email at fb.com
Daniel M. RoyUniversity of Toronto, Dept of Statistical Sciences; Computer ScienceVerified email at utoronto.ca
Jose Javier Gonzalez OrtizMassachusetts Institute of TechnologyVerified email at mit.edu
Alex RendaPhD Student, MITVerified email at csail.mit.edu
Peter-Michael OseraGrinnell CollegeVerified email at cs.grinnell.edu
Daniel J. Weitzner3Com Founders Senior Research Scientist, Massachusetts Institute of TechnologyVerified email at csail.mit.edu
Sunoo ParkMITVerified email at csail.mit.edu
Steve ZdancewicProfessor of Computer and Information Science, University of PennsylvaniaVerified email at cis.upenn.edu
David WalkerPrinceton UniversityVerified email at cs.princeton.edu
Jonathan S. RosenfeldPhD, Computer Science, MITVerified email at csail.mit.edu
Saman AmarasingheMITVerified email at mit.edu
Riyadh BaghdadiAssistant Professor (NYUAD); Global Network Assistant Professor (NYU); Research Affiliate (MIT)Verified email at mit.edu
BENHAMIDA Fatima ZohraAssistant professor at Ecole Nationale Superieure d'InformatiqueVerified email at esi.dz
Abdelkader Nadir DebbaghEtudiant, Ecole Nationale Supérieure d'InformatiqueVerified email at esi.dz
David BauAssistant Professor at Northeastern UniversityVerified email at northeastern.edu

Jonathan Frankle

Massachusetts Institute of Technology

Verified email at mit.edu - Homepage

Empirical Deep Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks J Frankle, M Carbin International Conference on Learning Representations, 2019	2969	2019
What is the State of Neural Network Pruning? D Blalock, JJG Ortiz, J Frankle, J Guttag Conference on Machine Learning and Systems, 2020	853	2020
Comparing Rewinding and Fine-tuning in Neural Network Pruning A Renda, J Frankle, M Carbin International Conference on Learning Representations, 2020	317	2020
Linear Mode Connectivity and the Lottery Ticket Hypothesis J Frankle, GK Dziugaite, DM Roy, M Carbin International Conference on Machine Learning, 2020	312	2020
Stabilizing the Lottery Ticket Hypothesis / The Lottery Ticket Hypothesis at Scale J Frankle, GK Dziugaite, DM Roy, M Carbin arXiv, 2019	304*	2019
The Lottery Ticket Hypothesis for Pre-Trained BERT Networks T Chen, J Frankle, S Chang, S Liu, Y Zhang, Z Wang, M Carbin Neural Information Processing Systems, 2020	262	2020
The Perpetual Line-Up: Unregulated Police Face Recognition in America C Garvie, A Bedoya, J Frankle Georgetown Law, Center on Privacy & Technology, 2016	261	2016
Pruning Neural Networks at Initialization: Why are We Missing the Mark? J Frankle, GK Dziugaite, DM Roy, M Carbin International Conference on Learning Representations, 2021	150	2021
The Early Phase of Neural Network Training J Frankle, DJ Schwab, AS Morcos International Conference on Learning Representations, 2020	138	2020
Example-Directed Synthesis: A Type-Theoretic Interpretation J Frankle, PM Osera, D Walker, S Zdancewic POPL 51 (1), 802-815, 2016	136	2016
Facial-Recognition Software Might Have a Racial Bias Problem C Garvie, J Frankle The Atlantic 7, 2016	108	2016
The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models T Chen, J Frankle, S Chang, S Liu, Y Zhang, M Carbin, Z Wang Conference on Computer Vision and Pattern Recognition, 2021	101	2021
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs J Frankle, DJ Schwab, AS Morcos International Conference on Learning Representations, 2021	99	2021
Practical Accountability of Secret Processes J Frankle, S Park, D Shaar, S Goldwasser, D Weitzner 27th USENIX Security Symposium (USENIX Security 18), 657-674, 2018	78	2018
The perpetual line-up C Garvie, A Bedoya, J Frankle Georgetown Law Center on Privacy & Technology 18, 2016	50	2016
Desirable Inefficiency P Ohm, J Frankle Fla. L. Rev. 70, 777, 2018	49*	2018
Are all negatives created equal in contrastive instance discrimination? TT Cai, J Frankle, DJ Schwab, AS Morcos Science Meets Engineering of Deep Learning Workshop (ICLR), 2021	47*	2021
On the Predictability of Pruning Across Scales JS Rosenfeld, J Frankle, M Carbin, N Shavit International Conference on Machine Learning, 2021	27	2021
Why King George III Can Encrypt W Tong, S Gold, S Gichohi, M Roman, J Frankle Freedom to Tinker, 2014	18	2014
Revisiting "Qualitatively Characterizing Neural Network Optimization Problems" J Frankle Workshop on Deep Learning through Information Geometry (NeurIPS 2020), 2020	15	2020

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