受强制性开放获取政策约束的文章 - Mehrdad Mahdavi了解详情
可在其他位置公开访问的文章：25 篇
Federated learning with compression: Unified analysis and sharp guarantees
F Haddadpour, MM Kamani, A Mokhtari, M Mahdavi
International Conference on Artificial Intelligence and Statistics, 2350-2358, 2021
强制性开放获取政策： US National Science Foundation
Local sgd with periodic averaging: Tighter analysis and adaptive synchronization
F Haddadpour, MM Kamani, M Mahdavi, V Cadambe
Advances in Neural Information Processing Systems 32, 2019
强制性开放获取政策： US National Science Foundation
Distributionally Robust Federated Averaging
Y Deng, MM Kamani, M Mahdavi
Advances in Neural Information Processing Systems 33, 2020
强制性开放获取政策： US National Science Foundation
On Provable Benefits of Depth in Training Graph Convolutional Networks
W Cong, M Ramezani, M Mahdavi
Advances in Neural Information Processing Systems 34, 2021
强制性开放获取政策： US National Science Foundation
Trading redundancy for communication: Speeding up distributed SGD for non-convex optimization
F Haddadpour, MM Kamani, M Mahdavi, V Cadambe
International Conference on Machine Learning, 2545-2554, 2019
强制性开放获取政策： US National Science Foundation
Local stochastic gradient descent ascent: Convergence analysis and communication efficiency
Y Deng, M Mahdavi
International Conference on Artificial Intelligence and Statistics, 1387-1395, 2021
强制性开放获取政策： US National Science Foundation
Predicting protein–ligand docking structure with graph neural network
H Jiang, J Wang, W Cong, Y Huang, M Ramezani, A Sarma, ...
Journal of chemical information and modeling 62 (12), 2923-2932, 2022
强制性开放获取政策： US National Science Foundation, US National Institutes of Health
Meta-learning with an Adaptive Task Scheduler
H Yao, Y Wang, Y Wei, P Zhao, M Mahdavi, D Lian, C Finn
Advances in Neural Information Processing Systems 34, 2021
强制性开放获取政策： 国家自然科学基金委员会
Gcn meets gpu: Decoupling “when to sample” from “how to sample”
M Ramezani, W Cong, M Mahdavi, A Sivasubramaniam, M Kandemir
Advances in Neural Information Processing Systems 33, 18482-18492, 2020
强制性开放获取政策： US National Science Foundation, US Department of Defense
Online Structured Meta-learning
H Yao, Y Zhou, M Mahdavi, Z Li, R Socher, C Xiong
Advances in Neural Information Processing Systems 33, 2020
强制性开放获取政策： US National Science Foundation
GPU-accelerated flexible molecular docking
M Fan, J Wang, H Jiang, Y Feng, M Mahdavi, K Madduri, MT Kandemir, ...
The Journal of Physical Chemistry B 125 (4), 1049-1060, 2021
强制性开放获取政策： US National Institutes of Health
Guiding conventional protein–ligand docking software with convolutional neural networks
H Jiang, M Fan, J Wang, A Sarma, S Mohanty, NV Dokholyan, M Mahdavi, ...
Journal of chemical information and modeling 60 (10), 4594-4602, 2020
强制性开放获取政策： US National Science Foundation, US National Institutes of Health
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection
W Cong, M Mahdavi
International Conference on Artificial Intelligence and Statistics (AISTAT), 2023
强制性开放获取政策： US National Science Foundation
Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time
Y Deng, M Mahdavi
Artificial Intelligence and Statistics, 2022
强制性开放获取政策： US National Science Foundation
Targeted data-driven regularization for out-of-distribution generalization
MM Kamani, S Farhang, M Mahdavi, JZ Wang
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020
强制性开放获取政策： US National Science Foundation
Tight Analysis of Extra-gradient and Optimistic Gradient Methods For Nonconvex Minimax Problems
P Mahdavinia, Y Deng, H Li, M Mahdavi
Advances in Neural Information Processing Systems 34, 2022
强制性开放获取政策： US National Science Foundation
Learning to quantize deep neural networks: A competitive-collaborative approach
MFF Khan, MM Kamani, M Mahdavi, V Narayanan
2020 57th ACM/IEEE Design Automation Conference (DAC), 1-6, 2020
强制性开放获取政策： US Department of Defense
Targeted meta-learning for critical incident detection in weather data
MM Kamani, S Farhang, M Mahdavi, JZ Wang
International Conference on Machine Learning, Workshop on" Climate Change …, 2019
强制性开放获取政策： US National Science Foundation
DyFormer: A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability
W Cong, Y Wu, Y Tian, M Gu, Y Xia, CJ Chen, M Mahdavi
SIAM International Conference on Data Mining (SDM), 2023
强制性开放获取政策： US National Science Foundation
Understanding Deep Gradient Leakage via Inversion Influence Functions
H Zhang, J Hong, Y Deng, M Mahdavi, J Zhou
Advances in Neural Information Processing Systems (NeurIPS), 2023
强制性开放获取政策： US National Science Foundation, US Department of Defense, US National …
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