Jonathan Frankle
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
1102*2019
The Perpetual Line-Up: Unregulated Police Face Recognition in America
C Garvie, A Bedoya, J Frankle
Georgetown Law, Center on Privacy & Technology, 2016
1722016
What is the State of Neural Network Pruning?
D Blalock, JJG Ortiz, J Frankle, J Guttag
Conference on Machine Learning and Systems, 2020
1362020
Stabilizing the Lottery Ticket Hypothesis / The Lottery Ticket Hypothesis at Scale
J Frankle, GK Dziugaite, DM Roy, M Carbin
arXiv, 2019
117*2019
Example-Directed Synthesis: A Type-Theoretic Interpretation
J Frankle, PM Osera, D Walker, S Zdancewic
POPL 51 (1), 802-815, 2016
952016
Comparing Rewinding and Fine-tuning in Neural Network Pruning
A Renda, J Frankle, M Carbin
International Conference on Learning Representations, 2020
622020
Facial-Recognition Software Might Have a Racial Bias Problem
C Garvie, J Frankle
The Atlantic 7, 2016
532016
Linear Mode Connectivity and the Lottery Ticket Hypothesis
J Frankle, GK Dziugaite, DM Roy, M Carbin
International Conference on Machine Learning, 2020
462020
The Early Phase of Neural Network Training
J Frankle, DJ Schwab, AS Morcos
International Conference on Learning Representations, 2020
372020
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
292018
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
282020
Desirable Inefficiency
P Ohm, J Frankle
Fla. L. Rev. 70, 777, 2018
22*2018
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
192021
Why King George III Can Encrypt
W Tong, S Gold, S Gichohi, M Roman, J Frankle
Freedom to Tinker, 2014
162014
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
14*2021
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
72021
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
42021
Tiramisu: A polyhedral compiler for dense and sparse deep learning
R Baghdadi, AN Debbagh, K Abdous, FZ Benhamida, A Renda, ...
arXiv preprint arXiv:2005.04091, 2020
42020
Dissecting Pruned Neural Networks
J Frankle, D Bau
Workshop on Debugging Machine Learning (ICLR 2019), 2019
32019
How Russia’s new facial recognition app could end anonymity
J Frankle
The Atlantic, 2016
32016
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