Zobrazeno 1 - 10
of 114 024
pro vyhledávání: '"Ahmadi, A. A."'
The classic Resource Constrained Shortest Path (RCSP) problem aims to find a cost optimal path between a pair of nodes in a network such that the resources used in the path are within a given limit. Having been studied for over a decade, RCSP has see
Externí odkaz:
http://arxiv.org/abs/2412.13888
Autor:
Idahl, Maximilian, Ahmadi, Zahra
We present OpenReviewer, an open-source system for generating high-quality peer reviews of machine learning and AI conference papers. At its core is Llama-OpenReviewer-8B, an 8B parameter language model specifically fine-tuned on 79,000 expert review
Externí odkaz:
http://arxiv.org/abs/2412.11948
Autor:
Taghizadeh, Maryam, Ahmadi, Mahmood
Vehicular fog computing (VFC) can be considered as an important alternative to address the existing challenges in intelligent transportation systems (ITS). The main purpose of VFC is to perform computational tasks through various vehicles. At present
Externí odkaz:
http://arxiv.org/abs/2412.11230
We propose a scheme to enhance quantum entanglement in an optomechanical system consisting of two mechanically coupled mechanical resonators, which are driven by a common electromagnetic field. Each mechanical resonator is linearly and quadratically
Externí odkaz:
http://arxiv.org/abs/2412.08288
The rapid advancement of Generative AI (Gen AI) technologies, particularly tools like ChatGPT, is significantly impacting the labor market by reshaping job roles and skill requirements. This study examines the demand for ChatGPT-related skills in the
Externí odkaz:
http://arxiv.org/abs/2412.07042
In this paper we propose efficient randomized fixed-precision techniques for low tubal rank approximation of tensors. The proposed methods are faster and more efficient than the existing fixed-precision algorithms for approximating the truncated tens
Externí odkaz:
http://arxiv.org/abs/2412.02598
This paper proposes fast randomized algorithms for computing the Kronecker Tensor Decomposition (KTD). The proposed algorithms can decompose a given tensor into the KTD format much faster than the existing state-of-the-art algorithms. Our principal i
Externí odkaz:
http://arxiv.org/abs/2412.02597
This paper introduces a novel collaborative neurodynamic model for computing nonnegative Canonical Polyadic Decomposition (CPD). The model relies on a system of recurrent neural networks to solve the underlying nonconvex optimization problem associat
Externí odkaz:
http://arxiv.org/abs/2411.18127
Quantum batteries are emerging as highly efficient energy storage devices that can exceed classical performance limits. Although there have been significant advancements in controlling these systems, challenges remain in stabilizing stored energy and
Externí odkaz:
http://arxiv.org/abs/2411.16633
We investigate the concept of algorithmic replicability introduced by Impagliazzo et al. 2022, Ghazi et al. 2021, Ahn et al. 2024 in an online setting. In our model, the input sequence received by the online learner is generated from time-varying dis
Externí odkaz:
http://arxiv.org/abs/2411.13730