David Rolnick

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Lynn H. KaackHertie SchoolAdresse e-mail validée de hertie-school.org
Priya L. DontiAssistant Professor, MIT EECS & LIDS; Co-founder and Chair, Climate Change AIAdresse e-mail validée de mit.edu
Nikola Milojevic-DupontMCC BerlinAdresse e-mail validée de mcc-berlin.net
Konrad Paul KordingNathan Mossell University Professor (PIK), bioengineering & neuroscience, UPenn, CIFAR LMBAdresse e-mail validée de upenn.edu
Kelly KochanskiPhD, University of Colorado - BoulderAdresse e-mail validée de colorado.edu
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARAdresse e-mail validée de umontreal.ca
S. Karthik MukkavilliGroup Head of AI at Mercuria | Principal ScientistAdresse e-mail validée de mercuria.com
Evan D. SherwinLawrence Berkeley National LaboratoryAdresse e-mail validée de lbl.gov
Sasha LuccioniHugging FaceAdresse e-mail validée de huggingface.co
Anna Waldman-BrownMIT Industrial Performance CenterAdresse e-mail validée de alum.mit.edu
Max TegmarkProfessor of Physics, MITAdresse e-mail validée de mit.edu
Serge BelongieUniversity of CopenhagenAdresse e-mail validée de di.ku.dk
Felix CreutzigMercator Research Institute on Global Commons and Climate Change & TUB, BerlinAdresse e-mail validée de mcc-berlin.net
Kris SankaranUniversity of Wisconsin - MadisonAdresse e-mail validée de wisc.edu
Andrew RossPhD student, Harvard UniversityAdresse e-mail validée de g.harvard.edu
John C. PlattGoogleAdresse e-mail validée de google.com
Boris HaninAssistant Professor, Princeton ORFEAdresse e-mail validée de princeton.edu
Natasha JaquesUniversity of Washington, Google ResearchAdresse e-mail validée de google.com
Andrew NgStanford UniversityAdresse e-mail validée de cs.stanford.edu
Alexandre LacosteStaff Research Scientist, ServiceNow ResearchAdresse e-mail validée de servicenow.com

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David Rolnick

McGill University, Mila Quebec AI Institute

Adresse e-mail validée de cs.mcgill.ca - Page d'accueil

Machine Learning Climate Change Biodiversity Deep Learning Theory


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
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	1394*	2022
Experience replay for continual learning D Rolnick, A Ahuja, J Schwarz, TP Lillicrap, G Wayne Advances in Neural Information Processing Systems, 2019	1282	2019
Why does deep and cheap learning work so well? HW Lin, M Tegmark, D Rolnick Journal of Statistical Physics 168, 1223-1247, 2017	826	2017
Deep learning is robust to massive label noise D Rolnick, A Veit, S Belongie, N Shavit arXiv preprint arXiv:1705.10694, 2017	737	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	363	2022
How to start training: The effect of initialization and architecture B Hanin, D Rolnick Advances in Neural Information Processing Systems, 2018	310	2018
Complexity of linear regions in deep networks B Hanin, D Rolnick International Conference on Machine Learning, 2596-2604, 2019	275	2019
Deep ReLU networks have surprisingly few activation patterns B Hanin, D Rolnick Advances in Neural Information Processing Systems, 361-370, 2019	275	2019
The power of deeper networks for expressing natural functions D Rolnick, M Tegmark International Conference on Learning Representations, 2018	246	2018
DC3: A learning method for optimization with hard constraints PL Donti, D Rolnick, JZ Kolter International Conference on Learning Representations, 2021	231	2021
Measuring and regularizing networks in function space AS Benjamin, D Rolnick, K Kording International Conference on Learning Representations, 2019	172	2019
Reverse-engineering deep ReLU networks D Rolnick, K Kording International Conference on Machine Learning, 8178-8187, 2020	131	2020
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	82	2024
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	81	2022
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	67	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	55	2023
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	46	2019
Bugs in the data: How ImageNet misrepresents biodiversity AS Luccioni, D Rolnick AAAI Conference on Artificial Intelligence, 2022	45	2022
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	45	2016
Pushing the frontiers in climate modelling and analysis with machine learning V Eyring, WD Collins, P Gentine, EA Barnes, M Barreiro, T Beucler, ... Nature Climate Change 14 (9), 916-928, 2024	42	2024

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