4 citations · 9 across the 18 of their papers we have counts for
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Enhancing Diffusion Models for Inverse Problems with Covariance-Aware Posterior Sampling
Shayan Mohajer Hamidi, En-Hui Yang
Inverse problems exist in many disciplines of science and engineering. In computer vision, for example, tasks such as inpainting, deblurring, and super resolution can be effectivel…
GP-FL: Model-Based Hessian Estimation for Second-Order Over-the-Air Federated Learning
Shayan Mohajer Hamidi, Ali Bereyhi, Saba Asaad +1
Second-order methods are widely adopted to improve the convergence rate of learning algorithms. In federated learning (FL), these methods require the clients to share their local H…
Rate-Constrained Quantization for Communication-Efficient Federated Learning
Shayan Mohajer Hamidi, Ali Bereyhi
Quantization is a common approach to mitigate the communication cost of federated learning (FL). In practice, the quantized local parameters are further encoded via an entropy codi…
How to Train the Teacher Model for Effective Knowledge Distillation
Shayan Mohajer Hamidi, Xizhen Deng, Renhao Tan +2
Recently, it was shown that the role of the teacher in knowledge distillation (KD) is to provide the student with an estimate of the true Bayes conditional probability density (BCP…
Adversarial Training via Adaptive Knowledge Amalgamation of an Ensemble of Teachers
Shayan Mohajer Hamidi, Linfeng Ye
Adversarial training (AT) is a popular method for training robust deep neural networks (DNNs) against adversarial attacks. Yet, AT suffers from two shortcomings: (i) the robustness…
Robustness Against Adversarial Attacks via Learning Confined Adversarial Polytopes
Shayan Mohajer Hamidi, Linfeng Ye
Deep neural networks (DNNs) could be deceived by generating human-imperceptible perturbations of clean samples. Therefore, enhancing the robustness of DNNs against adversarial atta…
AdaFed: Fair Federated Learning via Adaptive Common Descent Direction
Shayan Mohajer Hamidi, En-Hui Yang
Federated learning (FL) is a promising technology via which some edge devices/clients collaboratively train a machine learning model orchestrated by a server. Learning an unfair mo…
Bayes Conditional Distribution Estimation for Knowledge Distillation Based on Conditional Mutual Information
Linfeng Ye, Shayan Mohajer Hamidi, Renhao Tan +1
It is believed that in knowledge distillation (KD), the role of the teacher is to provide an estimate for the unknown Bayes conditional probability distribution (BCPD) to be used i…