David Rolnick

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Lynn H. KaackHertie School在 hertie-school.org 的电子邮件经过验证
Priya L. DontiAssistant Professor, MIT EECS & LIDS; Co-founder and Chair, Climate Change AI在 mit.edu 的电子邮件经过验证
Nikola Milojevic-DupontMCC Berlin在 mcc-berlin.net 的电子邮件经过验证
Konrad Paul KordingNathan Mossell University Professor (PIK), bioengineering & neuroscience, UPenn, CIFAR LMB在 upenn.edu 的电子邮件经过验证
Kelly KochanskiPhD, University of Colorado - Boulder在 colorado.edu 的电子邮件经过验证
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFAR在 umontreal.ca 的电子邮件经过验证
S. Karthik MukkavilliMercuria在 mercuria.com 的电子邮件经过验证
Evan D. SherwinLawrence Berkeley National Laboratory在 lbl.gov 的电子邮件经过验证
Sasha LuccioniHugging Face在 huggingface.co 的电子邮件经过验证
Anna Waldman-BrownMIT Industrial Performance Center在 alum.mit.edu 的电子邮件经过验证
Max TegmarkProfessor of Physics, MIT在 mit.edu 的电子邮件经过验证
Felix CreutzigMercator Research Institute on Global Commons and Climate Change & TUB, Berlin在 mcc-berlin.net 的电子邮件经过验证
Kris SankaranUniversity of Wisconsin - Madison在 wisc.edu 的电子邮件经过验证
Andrew RossPhD student, Harvard University在 g.harvard.edu 的电子邮件经过验证
Boris HaninAssistant Professor, Princeton ORFE在 princeton.edu 的电子邮件经过验证
Natasha JaquesUniversity of Washington, Google Research在 google.com 的电子邮件经过验证
Andrew NgStanford University在 cs.stanford.edu 的电子邮件经过验证
Alexandre LacosteStaff Research Scientist, ServiceNow Research在 servicenow.com 的电子邮件经过验证
Tegan MaharajAssistant Professor at Mila在 polymtl.ca 的电子邮件经过验证
John C. PlattGoogle在 google.com 的电子邮件经过验证

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David Rolnick

McGill University, Mila Quebec AI Institute

在 cs.mcgill.ca 的电子邮件经过验证 - 首页

Machine Learning Climate Change Biodiversity Deep Learning Theory


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Tackling climate change with machine learning D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ... ACM Computing Surveys (CSUR) 55 (2), 1-96, 2022	1344*	2022
Experience replay for continual learning D Rolnick, A Ahuja, J Schwarz, TP Lillicrap, G Wayne Advances in Neural Information Processing Systems, 2019	1220	2019
Why does deep and cheap learning work so well? HW Lin, M Tegmark, D Rolnick Journal of Statistical Physics 168, 1223-1247, 2017	821	2017
Deep learning is robust to massive label noise D Rolnick arXiv preprint arXiv:1705.10694, 2017	726	2017
Aligning artificial intelligence with climate change mitigation LH Kaack, PL Donti, E Strubell, G Kamiya, F Creutzig, D Rolnick Nature Climate Change 12 (6), 518-527, 2022	343	2022
How to start training: The effect of initialization and architecture B Hanin, D Rolnick Advances in Neural Information Processing Systems, 2018	309	2018
Complexity of linear regions in deep networks B Hanin, D Rolnick International Conference on Machine Learning, 2596-2604, 2019	268	2019
Deep ReLU networks have surprisingly few activation patterns B Hanin, D Rolnick Advances in Neural Information Processing Systems, 361-370, 2019	259	2019
The power of deeper networks for expressing natural functions D Rolnick, M Tegmark International Conference on Learning Representations, 2018	242	2018
DC3: A learning method for optimization with hard constraints PL Donti, D Rolnick, JZ Kolter International Conference on Learning Representations, 2021	214	2021
Measuring and regularizing networks in function space AS Benjamin, D Rolnick, K Kording International Conference on Learning Representations, 2019	163	2019
Reverse-engineering deep ReLU networks D Rolnick, K Kording International Conference on Machine Learning, 8178-8187, 2020	129	2020
Digitalization and the Anthropocene F Creutzig, D Acemoglu, X Bai, PN Edwards, MJ Hintz, LH Kaack, S Kilkis, ... Annual review of environment and resources 47 (1), 479-509, 2022	76	2022
Stealing part of a production language model N Carlini, D Paleka, KD Dvijotham, T Steinke, J Hayase, AF Cooper, ... arXiv preprint arXiv:2403.06634, 2024	64	2024
Lightweight, pre-trained transformers for remote sensing timeseries G Tseng, R Cartuyvels, I Zvonkov, M Purohit, D Rolnick, H Kerner arXiv preprint arXiv:2304.14065, 2023	56	2023
Faenet: Frame averaging equivariant gnn for materials modeling AA Duval, V Schmidt, A Hernández-Garcıa, S Miret, FD Malliaros, ... International Conference on Machine Learning, 9013-9033, 2023	45	2023
A multi-pass approach to large-scale connectomics Y Meirovitch, A Matveev, H Saribekyan, D Budden, D Rolnick, G Odor, ... arXiv preprint arXiv:1612.02120, 2016	44	2016
Cross-classification clustering: An efficient multi-object tracking technique for 3-d instance segmentation in connectomics Y Meirovitch, L Mi, H Saribekyan, A Matveev, D Rolnick, N Shavit Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019	42	2019
Bugs in the data: How ImageNet misrepresents biodiversity AS Luccioni, D Rolnick AAAI Conference on Artificial Intelligence, 2022	40	2022
Hidden symmetries of ReLU networks E Grigsby, K Lindsey, D Rolnick International Conference on Machine Learning, 11734-11760, 2023	31	2023

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