Jonathan Frankle

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Michael CarbinMassachusetts Institute of TechnologyEmail yang diverifikasi di csail.mit.edu
Gintare Karolina DziugaiteGoogle DeepMindEmail yang diverifikasi di google.com
Daniel M. RoyResearch Director, Vector Institute; Prof., U. Toronto (Statistics, CS)Email yang diverifikasi di utoronto.ca
Jose Javier Gonzalez OrtizMassachusetts Institute of TechnologyEmail yang diverifikasi di mit.edu
Ari S. MorcosDatologyAIEmail yang diverifikasi di datologyai.com
Mansheej PaulResearch Scientist, DatabricksEmail yang diverifikasi di databricks.com
Alex RendaPhD Student, MITEmail yang diverifikasi di csail.mit.edu
Zhangyang (Atlas) WangXTX Markets & University of Texas at AustinEmail yang diverifikasi di utexas.edu
Brett W. LarsenDatabricks Mosaic ResearchEmail yang diverifikasi di stanford.edu
Peter-Michael OseraGrinnell CollegeEmail yang diverifikasi di cs.grinnell.edu
Daniel J. Weitzner3Com Founders Senior Research Scientist, Massachusetts Institute of TechnologyEmail yang diverifikasi di csail.mit.edu
Sunoo ParkMITEmail yang diverifikasi di csail.mit.edu
Steve ZdancewicProfessor of Computer and Information Science, University of PennsylvaniaEmail yang diverifikasi di cis.upenn.edu
David WalkerPrinceton UniversityEmail yang diverifikasi di cs.princeton.edu
Jonathan S. RosenfeldPhD, Computer Science, MITEmail yang diverifikasi di csail.mit.edu
Saman AmarasingheMITEmail yang diverifikasi di mit.edu
Riyadh BaghdadiAssistant Professor (NYUAD); Global Network Assistant Professor (NYU); Research Affiliate (MIT)Email yang diverifikasi di nyu.edu
BENHAMIDA Fatima ZohraAssistant professor at Ecole Nationale Superieure d'InformatiqueEmail yang diverifikasi di esi.dz
Abdelkader Nadir DebbaghEtudiant, Ecole Nationale Supérieure d'InformatiqueEmail yang diverifikasi di esi.dz
David BauAssistant Professor at Northeastern UniversityEmail yang diverifikasi di northeastern.edu

Ikuti

Jonathan Frankle

Databricks

Email yang diverifikasi di databricks.com - Beranda

Deep Learning


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks J Frankle, M Carbin International Conference on Learning Representations, 2019	4379	2019
What is the State of Neural Network Pruning? D Blalock, JJG Ortiz, J Frankle, J Guttag Conference on Machine Learning and Systems, 2020	1390	2020
Linear Mode Connectivity and the Lottery Ticket Hypothesis J Frankle, GK Dziugaite, DM Roy, M Carbin International Conference on Machine Learning, 2020	594	2020
Comparing Rewinding and Fine-tuning in Neural Network Pruning A Renda, J Frankle, M Carbin International Conference on Learning Representations, 2020	452	2020
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	409	2020
The Perpetual Line-Up: Unregulated Police Face Recognition in America C Garvie, A Bedoya, J Frankle Georgetown Law, Center on Privacy & Technology, 2016	404	2016
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	260	2021
The Early Phase of Neural Network Training J Frankle, DJ Schwab, AS Morcos International Conference on Learning Representations, 2020	189	2020
Example-Directed Synthesis: A Type-Theoretic Interpretation J Frankle, PM Osera, D Walker, S Zdancewic POPL 51 (1), 802-815, 2016	155	2016
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	144	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	143	2021
Facial-Recognition Software Might Have a Racial Bias Problem C Garvie, J Frankle The Atlantic 7, 2016	135	2016
Lora learns less and forgets less D Biderman, J Portes, JJG Ortiz, M Paul, P Greengard, C Jennings, ... arXiv preprint arXiv:2405.09673, 2024	91	2024
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	90	2018
Desirable Inefficiency P Ohm, J Frankle Fla. L. Rev. 70, 777, 2018	74	2018
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	69	2024
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	46	2022

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