6 papers
TenAd: A Tensor-based Low-rank Black Box Adversarial Attack for Video Classification
Kimia haghjooei, Mansoor Rezghi
Deep learning models have achieved remarkable success in computer vision but remain vulnerable to adversarial attacks, particularly in black-box settings where model details are un…
Heterogeneous Multi-Agent Reinforcement Learning via Mirror Descent Policy Optimization
Mohammad Mehdi Nasiri, Mansoor Rezghi
This paper presents an extension of the Mirror Descent method to overcome challenges in cooperative Multi-Agent Reinforcement Learning (MARL) settings, where agents have varying ab…
Multidimensional Data Analysis Based on Block Convolutional Tensor Decomposition
Mahdi Molavi, Mansoor Rezghi, Tayyebeh Saeedi
Tensor decompositions are powerful tools for analyzing multi-dimensional data in their original format. Besides tensor decompositions like Tucker and CP, Tensor SVD (t-SVD) which i…
Local and Global Structure Preservation Based Spectral Clustering
Kajal Eybpoosh, Mansoor Rezghi, Abbas Heydari
Spectral Clustering (SC) is widely used for clustering data on a nonlinear manifold. SC aims to cluster data by considering the preservation of the local neighborhood structure on…
An adaptive synchronization approach for weights of deep reinforcement learning
S. Amirreza Badran, Mansoor Rezghi
Deep Q-Networks (DQN) is one of the most well-known methods of deep reinforcement learning, which uses deep learning to approximate the action-value function. Solving numerous Deep…
A novel extension of Generalized Low-Rank Approximation of Matrices based on multiple-pairs of transformations
Soheil Ahmadi, Mansoor Rezghi
Dimensionality reduction is a main step in the learning process which plays an essential role in many applications. The most popular methods in this field like SVD, PCA, and LDA, o…