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Jonathan Frankle
Jonathan Frankle
Databricks
Email verificata su databricks.com - Home page
Titolo
Citata da
Citata da
Anno
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
J Frankle, M Carbin
International Conference on Learning Representations, 2019
43792019
What is the State of Neural Network Pruning?
D Blalock, JJG Ortiz, J Frankle, J Guttag
Conference on Machine Learning and Systems, 2020
13902020
Linear Mode Connectivity and the Lottery Ticket Hypothesis
J Frankle, GK Dziugaite, DM Roy, M Carbin
International Conference on Machine Learning, 2020
5942020
Comparing Rewinding and Fine-tuning in Neural Network Pruning
A Renda, J Frankle, M Carbin
International Conference on Learning Representations, 2020
4522020
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
4092020
The Perpetual Line-Up: Unregulated Police Face Recognition in America
C Garvie, A Bedoya, J Frankle
Georgetown Law, Center on Privacy & Technology, 2016
4042016
Stabilizing the Lottery Ticket Hypothesis / The Lottery Ticket Hypothesis at Scale
J Frankle, GK Dziugaite, DM Roy, M Carbin
arXiv, 2019
387*2019
Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs
MMLNLP Team
https://www.databricks.com/blog/mpt-7b, 2023
277*2023
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
2602021
The Early Phase of Neural Network Training
J Frankle, DJ Schwab, AS Morcos
International Conference on Learning Representations, 2020
1892020
Example-Directed Synthesis: A Type-Theoretic Interpretation
J Frankle, PM Osera, D Walker, S Zdancewic
POPL 51 (1), 802-815, 2016
1552016
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
1442021
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
1432021
Facial-Recognition Software Might Have a Racial Bias Problem
C Garvie, J Frankle
The Atlantic 7, 2016
1352016
Lora learns less and forgets less
D Biderman, J Portes, JJG Ortiz, M Paul, P Greengard, C Jennings, ...
arXiv preprint arXiv:2405.09673, 2024
912024
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
91*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
902018
Desirable Inefficiency
P Ohm, J Frankle
Fla. L. Rev. 70, 777, 2018
742018
The shift from models to compound ai systems
M Zaharia, O Khattab, L Chen, JQ Davis, H Miller, C Potts, J Zou, ...
Berkeley Artificial Intelligence Research Lab. Available online at: https …, 2024
692024
Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask?
M Paul, F Chen, BW Larsen, J Frankle, S Ganguli, GK Dziugaite
arXiv preprint arXiv:2210.03044, 2022
462022
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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