Kamyar Azizzadenesheli

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Anima AnandkumarCalifornia Institute of Technology and NVIDIAZweryfikowany adres z caltech.edu
Zongyi LiCaltechZweryfikowany adres z caltech.edu
Nikola KovachkiCalifornia Institute of TechnologyZweryfikowany adres z caltech.edu
Burigede LiuGranta Design Assistant Professor, University of CambridgeZweryfikowany adres z cam.ac.uk
Kaushik BhattacharyaCalifornia Institute of TechnologyZweryfikowany adres z caltech.edu
Andrew M StuartComputing + Mathematical Sciences, CaltechZweryfikowany adres z caltech.edu
Zachary C. LiptonRaj Reddy Associate Professor of Machine Learning @ Carnegie Mellon University; CTO + CSO @ AbridgeZweryfikowany adres z cmu.edu
Jean KossaifiSenior Research Scientist at NVIDIAZweryfikowany adres z nvidia.com
Karthik KashinathPrincipal Scientist & Engineer, NVIDIAZweryfikowany adres z nvidia.com
Zachary E. RossCalifornia Institute of TechnologyZweryfikowany adres z caltech.edu
Yisong YueCalifornia Institute of Technology; Asari AI; Latitude AIZweryfikowany adres z caltech.edu
Jeremy BernsteinCSAIL, MITZweryfikowany adres z mit.edu
Sahin LaleCaltechZweryfikowany adres z caltech.edu
Babak HassibiMose and Lilian S. Bohn Professor of Electrical EngineeringZweryfikowany adres z caltech.edu
Jaideep PathakNVIDIAZweryfikowany adres z nvidia.com
Soheil EsmaeilzadehFacebook AI | Stanford Ph.D.Zweryfikowany adres z stanford.edu
Peter HarringtonNERSC, Lawrence Berkeley National LaboratoryZweryfikowany adres z lbl.gov
Morteza MardaniNVIDIA Research & Stanford UniversityZweryfikowany adres z nvidia.com
Soon-Jo ChungBren Professor of Control and Dynamical Systems at Caltech; JPL Senior Research ScientistZweryfikowany adres z caltech.edu
Guanya ShiAssistant Professor, Robotics Institute, Carnegie Mellon UniversityZweryfikowany adres z andrew.cmu.edu

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Kamyar Azizzadenesheli

Nvidia

Zweryfikowany adres z nvidia.com - Strona główna

Machine Learning


Tytuł Sortuj wg cytatów Sortuj wg roku Sortuj wg tytułu	Cytowane przez Cytowane przez	Rok
Fourier neural operator for parametric partial differential equations Z Li, N Kovachki, K Azizzadenesheli, B Liu, K Bhattacharya, A Stuart, ... arXiv preprint arXiv:2010.08895, 2020	2876	2020
signSGD: Compressed optimisation for non-convex problems J Bernstein, YX Wang, K Azizzadenesheli, A Anandkumar International Conference on Machine Learning, 560-569, 2018	1216	2018
Neural operator: Learning maps between function spaces with applications to pdes N Kovachki, Z Li, B Liu, K Azizzadenesheli, K Bhattacharya, A Stuart, ... Journal of Machine Learning Research 24 (89), 1-97, 2023	1019	2023
Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators J Pathak, S Subramanian, P Harrington, S Raja, A Chattopadhyay, ... arXiv preprint arXiv:2202.11214, 2022	874*	2022
Neural operator: Graph kernel network for partial differential equations Z Li, N Kovachki, K Azizzadenesheli, B Liu, K Bhattacharya, A Stuart, ... arXiv preprint arXiv:2003.03485, 2020	759	2020
Stochastic activation pruning for robust adversarial defense GS Dhillon, K Azizzadenesheli, ZC Lipton, J Bernstein, J Kossaifi, ... International Conference on Learning Representations (ICLR) 2018, 2018	718	2018
Physics-informed neural operator for learning partial differential equations Z Li, H Zheng, N Kovachki, D Jin, H Chen, B Liu, K Azizzadenesheli, ... ACM/JMS Journal of Data Science 1 (3), 1-27, 2024	530	2024
Physics-informed machine learning: case studies for weather and climate modelling K Kashinath, M Mustafa, A Albert, JL Wu, C Jiang, S Esmaeilzadeh, ... Philosophical Transactions of the Royal Society A 379 (2194), 20200093, 2021	523	2021
Multipole Graph Neural Operator for Parametric Partial Differential Equations Z Li, N Kovachki, K Azizzadenesheli, B Liu, K Bhattacharya, A Stuart, ... Neural Information Processing Systems (Neurips) 2020, 2020	443	2020
U-FNO—An enhanced Fourier neural operator-based deep-learning model for multiphase flow G Wen, Z Li, K Azizzadenesheli, A Anandkumar, SM Benson Advances in Water Resources 163, 104180, 2022	397	2022
Neural lander: Stable drone landing control using learned dynamics G Shi, X Shi, M O’Connell, R Yu, K Azizzadenesheli, A Anandkumar, ... 2019 International Conference on Robotics and Automation (ICRA), 9784-9790, 2019	362	2019
Regularized learning for domain adaptation under label shifts K Azizzadenesheli, A Liu, F Yang, A Anandkumar arXiv preprint arXiv:1903.09734, 2019	254	2019
signSGD with Majority Vote is Communication Efficient and Fault Tolerant J Bernstein, J Zhao, K Azizzadenesheli, A Anandkumar International Conference on Learning Representations (ICLR) 2019, 2018	223	2018
Efficient Exploration through Bayesian Deep Q-Networks K Azizzadenesheli, A Anandkumar Neural Information Processing Systems (NIPS) 2017 Workshop, 2018	215	2018
Neural-fly enables rapid learning for agile flight in strong winds M O’Connell, G Shi, X Shi, K Azizzadenesheli, A Anandkumar, Y Yue, ... Science Robotics 7 (66), eabm6597, 2022	209	2022
Eikonet: Solving the eikonal equation with deep neural networks JD Smith, K Azizzadenesheli, ZE Ross IEEE Transactions on Geoscience and Remote Sensing 2020, 2020	183	2020
Reinforcement learning of POMDPs using spectral methods K Azizzadenesheli, A Lazaric, A Anandkumar 29th Annual Conference on Learning Theory (COLT) 2016, 2016	156	2016
Neural operator: Graph kernel network for partial differential equations A Anandkumar, K Azizzadenesheli, K Bhattacharya, N Kovachki, Z Li, ... ICLR 2020 Workshop on Integration of Deep Neural Models and Differential …, 2020	150	2020
Burigede liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar. Fourier neural operator for parametric partial differential equations Z Li, NB Kovachki, K Azizzadenesheli International Conference on Learning Representations 2 (3), 4, 2021	139	2021
U-no: U-shaped neural operators MA Rahman, ZE Ross, K Azizzadenesheli https://arxiv.org/pdf/2204.11127, https://arxiv.org/pdf/2204.11127, 2022	136	2022

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