Zobrazeno 1 - 10
of 283
pro vyhledávání: '"Khalaj, Babak"'
Autor:
Saberi, Amir Hossein, Najafi, Amir, Emrani, Ala, Behjati, Amin, Zolfimoselo, Yasaman, Shadrooy, Mahdi, Motahari, Abolfazl, Khalaj, Babak H.
The aim of this paper is to address the challenge of gradual domain adaptation within a class of manifold-constrained data distributions. In particular, we consider a sequence of $T\ge2$ data distributions $P_1,\ldots,P_T$ undergoing a gradual shift,
Externí odkaz:
http://arxiv.org/abs/2410.14061
As artificial intelligence (AI) applications continue to expand in next-generation networks, there is a growing need for deep neural network (DNN) models. Although DNN models deployed at the edge are promising for providing AI as a service with low l
Externí odkaz:
http://arxiv.org/abs/2401.00631
Autor:
Saberi, Amir Hossein, Najafi, Amir, Heidari, Alireza, Movasaghinia, Mohammad Hosein, Motahari, Abolfazl, Khalaj, Babak H.
We propose a novel framework for incorporating unlabeled data into semi-supervised classification problems, where scenarios involving the minimization of either i) adversarially robust or ii) non-robust loss functions have been considered. Notably, w
Externí odkaz:
http://arxiv.org/abs/2310.00027
Autor:
Kalkhoran, Seyyed Amirhossein Ameli, Letafati, Mehdi, Erdemir, Ecenaz, Khalaj, Babak Hossein, Behroozi, Hamid, Gündüz, Deniz
In this paper, a generalization of deep learning-aided joint source channel coding (Deep-JSCC) approach to secure communications is studied. We propose an end-to-end (E2E) learning-based approach for secure communication against multiple eavesdropper
Externí odkaz:
http://arxiv.org/abs/2308.02892
In this article, the authors provide a comprehensive overview on three core pillars of metaverse-as-a-service (MaaS) platforms; privacy and security, edge computing, and blockchain technology. The article starts by investigating security aspects for
Externí odkaz:
http://arxiv.org/abs/2301.01221
In this paper, we consider multiple cache-enabled end-users connected to multiple transmitters through a linear network. We also prevent a totally passive eavesdropper, who sniffs the packets in the delivery phase, from obtaining any information abou
Externí odkaz:
http://arxiv.org/abs/2211.14672
In this paper, we find a sample complexity bound for learning a simplex from noisy samples. Assume a dataset of size $n$ is given which includes i.i.d. samples drawn from a uniform distribution over an unknown simplex in $\mathbb{R}^K$, where samples
Externí odkaz:
http://arxiv.org/abs/2209.05953
Publikováno v:
In Computer Networks November 2024 253
Publikováno v:
In Signal Processing February 2025 227
Autor:
Karami, Farzan, Khalaj, Babak Hossein
Publikováno v:
In Computer Communications 1 December 2024 228