Zobrazeno 1 - 10
of 381 645
pro vyhledávání: '"Ismail, A."'
Dilated Convolution with Learnable Spacing (DCLS) is a recent advanced convolution method that allows enlarging the receptive fields (RF) without increasing the number of parameters, like the dilated convolution, yet without imposing a regular grid.
Externí odkaz:
http://arxiv.org/abs/2408.03164
This study investigates the realm of liquid neural networks (LNNs) and their deployment on neuromorphic hardware platforms. It provides an in-depth analysis of Liquid State Machines (LSMs) and explores the adaptation of LNN architectures to neuromorp
Externí odkaz:
http://arxiv.org/abs/2407.20590
Autor:
Zhao, Yufei, Feng, Yuan, Ismail, Afkar Mohamed, Wang, Ziyue, Guan, Yong Liang, Guo, Yongxin, Yuen, Chau
This paper presents a sophisticated reconfigurable metasurface architecture that introduces an advanced concept of flexible full-array space-time wavefront manipulation with enhanced dynamic capabilities. The practical 2-bit phase-shifting unit cell
Externí odkaz:
http://arxiv.org/abs/2407.19379
Multidimensional Big Data Analytics is an emerging area that marries the capabilities of OLAP with modern Big Data Analytics. Essentially, the idea is engrafting multidimensional models into Big Data analytics processes to gain into expressive power
Externí odkaz:
http://arxiv.org/abs/2407.18604
Autor:
Puppala, Sai, Hossain, Ismail, Alam, Md Jahangir, Talukder, Sajedul, Talukder, Zahidur, Bahauddin, Syed
Federated Learning (FL) has emerged as a transformative approach for enabling distributed machine learning while preserving user privacy, yet it faces challenges like communication inefficiencies and reliance on centralized infrastructures, leading t
Externí odkaz:
http://arxiv.org/abs/2407.18387
Autor:
Puppala, Sai, Hossain, Ismail, Alam, Md Jahangir, Talukder, Sajedul, Ferdaus, Jannatul, Hasan, Mahedi, Pisupati, Sameera, Mathukumilli, Shanmukh
Federated learning has become a significant approach for training machine learning models using decentralized data without necessitating the sharing of this data. Recently, the incorporation of generative artificial intelligence (AI) methods has prov
Externí odkaz:
http://arxiv.org/abs/2407.18358
Autor:
Özçil, İsmail, Koku, A. Buğra
The advancement in computing power has significantly reduced the training times for deep learning, fostering the rapid development of networks designed for object recognition. However, the exploration of object utility, which is the affordance of the
Externí odkaz:
http://arxiv.org/abs/2407.15479
Autor:
Tahiri, Ismail, Nuino, Ahmed
Our purpose of this paper is to focus on fixed point property in fuzzy metric space. To achieve our objective, we will introduce a new contraction condition to examine the fixed point for multi-valued mapping, then we will be investigating the obtain
Externí odkaz:
http://arxiv.org/abs/2407.15271
This paper addresses the critical issue of miscalibration in CLIP-based model adaptation, particularly in the challenging scenario of out-of-distribution (OOD) samples, which has been overlooked in the existing literature on CLIP adaptation. We empir
Externí odkaz:
http://arxiv.org/abs/2407.13588
Autor:
Brams, Steven J., Ismail, Mehmet S.
We introduce a novel system of matching and scoring players in tournaments, called Multi-Tier Tournaments, illustrated by chess and based on the following rules: 1. Players are divided into skill-based tiers, based on their Elo ratings. 2. Starting w
Externí odkaz:
http://arxiv.org/abs/2407.13845