David Rolnick

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Lynn H. KaackHertie SchoolVerified email at hertie-school.org
Priya L. DontiAssistant Professor, MIT EECS & LIDS; Co-founder and Chair, Climate Change AIVerified email at mit.edu
Nikola Milojevic-DupontMCC BerlinVerified email at mcc-berlin.net
Konrad Paul KordingNathan Mossell University Professor (PIK), bioengineering & neuroscience, UPenn, CIFAR LMBVerified email at upenn.edu
Kelly KochanskiPhD, University of Colorado - BoulderVerified email at colorado.edu
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
S. Karthik MukkavilliIBM ResearchVerified email at ibm.com
Evan D. SherwinLawrence Berkeley National LaboratoryVerified email at lbl.gov
Sasha LuccioniHugging FaceVerified email at huggingface.co
Anna Waldman-BrownMIT Industrial Performance CenterVerified email at alum.mit.edu
Max TegmarkProfessor of Physics, MITVerified email at mit.edu
Felix CreutzigMercator Research Institute on Global Commons and Climate Change & TUB, BerlinVerified email at mcc-berlin.net
Kris SankaranUniversity of Wisconsin - MadisonVerified email at wisc.edu
Andrew RossPhD student, Harvard UniversityVerified email at g.harvard.edu
Boris HaninAssistant Professor, Princeton ORFEVerified email at princeton.edu
Natasha JaquesUniversity of Washington, Google ResearchVerified email at google.com
Andrew NgStanford UniversityVerified email at cs.stanford.edu
Alexandre LacosteStaff Research Scientist, ServiceNow ResearchVerified email at servicenow.com
Tegan MaharajAssistant Professor at MilaVerified email at polymtl.ca
John C. PlattGoogleVerified email at google.com

David Rolnick

McGill University, Mila Quebec AI Institute

Verified email at cs.mcgill.ca - Homepage

Machine Learning Climate Change Biodiversity Deep Learning Theory


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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	1254*	2022
Experience replay for continual learning D Rolnick, A Ahuja, J Schwarz, TP Lillicrap, G Wayne Advances in Neural Information Processing Systems, 2019	1144	2019
Why does deep and cheap learning work so well? HW Lin, M Tegmark, D Rolnick Journal of Statistical Physics 168, 1223-1247, 2017	801	2017
Deep learning is robust to massive label noise D Rolnick arXiv preprint arXiv:1705.10694, 2017	697	2017
How to start training: The effect of initialization and architecture B Hanin, D Rolnick Advances in Neural Information Processing Systems, 2018	300	2018
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	291	2022
Complexity of linear regions in deep networks B Hanin, D Rolnick International Conference on Machine Learning, 2596-2604, 2019	257	2019
Deep ReLU networks have surprisingly few activation patterns B Hanin, D Rolnick Advances in Neural Information Processing Systems, 361-370, 2019	250	2019
The power of deeper networks for expressing natural functions D Rolnick, M Tegmark International Conference on Learning Representations, 2018	234	2018
DC3: A learning method for optimization with hard constraints PL Donti, D Rolnick, JZ Kolter International Conference on Learning Representations, 2021	196	2021
Measuring and regularizing networks in function space AS Benjamin, D Rolnick, K Kording International Conference on Learning Representations, 2019	157	2019
Reverse-engineering deep ReLU networks D Rolnick, K Kording International Conference on Machine Learning, 8178-8187, 2020	125	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	60	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	51	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	46	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	43	2016
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	41	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	41	2019
Bugs in the data: How ImageNet misrepresents biodiversity AS Luccioni, D Rolnick AAAI Conference on Artificial Intelligence, 2022	38	2022
Climate change and AI: Recommendations for government action P Clutton-Brock, D Rolnick, PL Donti, L Kaack GPAI, Climate Change AI, Centre for AI & Climate, 2021	27*	2021

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