Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Zico Kolter"'
Autor:
Lars A Bratholm, Will Gerrard, Brandon Anderson, Shaojie Bai, Sunghwan Choi, Lam Dang, Pavel Hanchar, Addison Howard, Sanghoon Kim, Zico Kolter, Risi Kondor, Mordechai Kornbluth, Youhan Lee, Youngsoo Lee, Jonathan P Mailoa, Thanh Tu Nguyen, Milos Popovic, Goran Rakocevic, Walter Reade, Wonho Song, Luka Stojanovic, Erik H Thiede, Nebojsa Tijanic, Andres Torrubia, Devin Willmott, Craig P Butts, David R Glowacki
Publikováno v:
PLoS ONE, Vol 16, Iss 7, p e0253612 (2021)
The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in adva
Externí odkaz:
https://doaj.org/article/7bd641a416ff403b90f3c0a0e9726773
Publikováno v:
IEEE Open Journal of Control Systems, Vol 1, Pp 126-140 (2022)
Analyzing the worst-case performance of deep neural networks against input perturbations amounts to solving a large-scale non-convex optimization problem, for which several past works have proposed convex relaxations as a promising alternative. Howev
Externí odkaz:
https://doaj.org/article/b3ba3c31b7f74537b58c652cd13bc45c
Publikováno v:
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ab1e423c2b25169be401172084e7beee
https://doi.org/10.1109/wacv56688.2023.00464
https://doi.org/10.1109/wacv56688.2023.00464
Autor:
J. Zico Kolter
Publikováno v:
Science (New York, N.Y.). 378(6624)
Is ignoring everything that is known about code the best way to write programs?
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa53a3e24ae3683ad746bb094bab57e4
https://pubmed.ncbi.nlm.nih.gov/36480633
https://pubmed.ncbi.nlm.nih.gov/36480633
Autor:
Priya L. Donti, J. Zico Kolter
Publikováno v:
Annual Review of Environment and Resources. 46:719-747
In recent years, machine learning has proven to be a powerful tool for deriving insights from data. In this review, we describe ways in which machine learning has been leveraged to facilitate the development and operation of sustainable energy system
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::177ff91af665d1bafd79c6f5d169dadb
https://doi.org/10.1146/annurev-environ-020220-061831
https://doi.org/10.1146/annurev-environ-020220-061831
Publikováno v:
CVPR
Beyond achieving high performance across many vision tasks, multimodal models are expected to be robust to single-source faults due to the availability of redundant information between modalities. In this paper, we investigate the robustness of multi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7a93a635a4efdb934d0db187f17be153
http://arxiv.org/abs/2206.12714
http://arxiv.org/abs/2206.12714
Autor:
Kirill Shmilovich, Devin Willmott, Ivan Batalov, Mordechai Kornbluth, Jonathan Mailoa, J. Zico Kolter
Leveraging ab initio data at scale has enabled the development of machine learning models capable of extremely accurate and fast molecular property prediction. A central paradigm of many previous studies focuses on generating predictions for only a f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb1e30edcb14124d70e0bea3b7620438
http://arxiv.org/abs/2205.06133
http://arxiv.org/abs/2205.06133
Many recent state-of-the-art (SOTA) optical flow models use finite-step recurrent update operations to emulate traditional algorithms by encouraging iterative refinements toward a stable flow estimation. However, these RNNs impose large computation a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::11876dee09b547289834d24c5dad8785
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031198328
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::20ce7b2fa6cb1423059bb2c46a0e42e2
https://doi.org/10.1007/978-3-031-19833-5_4
https://doi.org/10.1007/978-3-031-19833-5_4
Autor:
Bistra Dilkina, Bart Selman, Daniel Freund, Warren B. Powell, Stefano Ermon, Steve Kelling, Angela K. Fuller, Alexander S. Flecker, John S. Selker, Carla P. Gomes, Douglas H. Fisher, Yexiang Xue, Milind Tambe, Mary Lou Zeeman, Fei Fang, Xiaoli Z. Fern, Christopher B. Barrett, Xiaojian Wu, John M. Gregoire, Alan Fern, Zico Kolter, John E. Hopcroft, Daniel Fink, Andrew Farnsworth, David B. Shmoys, Jon M. Conrad, Nicole D. Sintov, Thomas G. Dietterich, Abdul-Aziz Yakubu, Amulya Yadav, Daniel Sheldon, Christopher L. Wood, Weng-Keen Wong
Publikováno v:
Communications of the ACM. 62:56-65
Computer and information scientists join forces with other fields to help solve societal and environmental challenges facing humanity, in pursuit of a sustainable future.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7c1226eb22d7f55e7d9b7069cc82f5bc
https://doi.org/10.1145/3339399
https://doi.org/10.1145/3339399