受强制性开放获取政策约束的文章 - Mohammad Taha Toghani了解详情
可在其他位置公开访问的文章：10 篇
Unbounded gradients in federated learning with buffered asynchronous aggregation
MT Toghani, CA Uribe
2022 58th Annual Allerton Conference on Communication, Control, and …, 2022
强制性开放获取政策： US National Science Foundation, US National Institutes of Health
Communication-efficient distributed cooperative learning with compressed beliefs
MT Toghani, CA Uribe
IEEE Transactions on Control of Network Systems 9 (3), 1215-1226, 2022
强制性开放获取政策： US National Institutes of Health
Scalable average consensus with compressed communications
MT Toghani, CA Uribe
2022 American Control Conference (ACC), 3412-3417, 2022
强制性开放获取政策： US National Institutes of Health
MP-Boost: Minipatch Boosting via Adaptive Feature and Observation Sampling
MT Toghani, GI Allen
2021 IEEE International Conference on Big Data and Smart Computing (BigComp …, 2021
强制性开放获取政策： US National Science Foundation, US National Institutes of Health
PersA-FL: personalized asynchronous federated learning
MT Toghani, S Lee, CA Uribe
Optimization Methods and Software, 1-38, 2023
强制性开放获取政策： US National Science Foundation
Pars-push: Personalized, asynchronous and robust decentralized optimization
MT Toghani, S Lee, CA Uribe
IEEE Control Systems Letters 7, 361-366, 2022
强制性开放获取政策： US National Institutes of Health
On first-order meta-reinforcement learning with moreau envelopes
MT Toghani, S Perez-Salazar, CA Uribe
2023 62nd IEEE Conference on Decision and Control (CDC), 4176-4181, 2023
强制性开放获取政策： US National Science Foundation
On arbitrary compression for decentralized consensus and stochastic optimization over directed networks
MT Toghani, CA Uribe
European Journal of Control 68, 100682, 2022
强制性开放获取政策： US National Institutes of Health
Local stochastic factored gradient descent for distributed quantum state tomography
JL Kim, MT Toghani, CA Uribe, A Kyrillidis
IEEE Control Systems Letters 7, 199-204, 2022
强制性开放获取政策： US National Science Foundation, US Department of Defense, US National …
Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations
D Pirhayatifard, MT Toghani, G Balakrishnan, CA Uribe
2023 57th Asilomar Conference on Signals, Systems, and Computers, 789-793, 2023
强制性开放获取政策： US National Science Foundation
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