Zobrazeno 1 - 10
of 68 601
pro vyhledávání: '"Ebrahimi SO"'
Publikováno v:
Springer, Lecture Notes in Computer Science (LNCS), Volume 14731, 2024
License plate detection (LPD) is essential for traffic management, vehicle tracking, and law enforcement but faces challenges like variable lighting and diverse font types, impacting accuracy. Traditionally reliant on image processing and machine lea
Externí odkaz:
http://arxiv.org/abs/2412.12572
Autor:
Ebrahimi, Fatima, Haywood, Alexander
Global stability of differentially rotating plasma is investigated using a generalized effective potential. We first, for a current-free system, obtain a general form of an effective potential in terms of the free energies of global curvature and gra
Externí odkaz:
http://arxiv.org/abs/2412.07742
We present the first demonstration of a nanofabricated photonic crystal made from the magnetic material yttrium iron garnet (YIG). YIG is a compelling material for quantum technologies due to its unique magnetic and optical properties; however, exper
Externí odkaz:
http://arxiv.org/abs/2412.05361
Autor:
Malakshan, Sahar Rahimi, Saadabadi, Mohammad Saeed Ebrahimi, Dabouei, Ali, Nasrabadi, Nasser M.
Dataset Condensation (DC) aims to reduce deep neural networks training efforts by synthesizing a small dataset such that it will be as effective as the original large dataset. Conventionally, DC relies on a costly bi-level optimization which prohibit
Externí odkaz:
http://arxiv.org/abs/2412.04748
Autor:
Chen, Justin Chih-Yao, Wang, Zifeng, Palangi, Hamid, Han, Rujun, Ebrahimi, Sayna, Le, Long, Perot, Vincent, Mishra, Swaroop, Bansal, Mohit, Lee, Chen-Yu, Pfister, Tomas
Reverse thinking plays a crucial role in human reasoning. Humans can reason not only from a problem to a solution but also in reverse, i.e., start from the solution and reason towards the problem. This often enhances overall reasoning performance as
Externí odkaz:
http://arxiv.org/abs/2411.19865
Reinforcement Learning (RL) has emerged as a powerful paradigm in Artificial Intelligence (AI), enabling agents to learn optimal behaviors through interactions with their environments. Drawing from the foundations of trial and error, RL equips agents
Externí odkaz:
http://arxiv.org/abs/2411.18892
Recent privacy regulations (e.g., GDPR) grant data subjects the `Right to Be Forgotten' (RTBF) and mandate companies to fulfill data erasure requests from data subjects. However, companies encounter great challenges in complying with the RTBF regulat
Externí odkaz:
http://arxiv.org/abs/2411.17126
Autor:
Thakkar, Denisha, Trinh, Vincent Quoc-Huy, Varma, Sonal, Kahou, Samira Ebrahimi, Rivaz, Hassan, Hosseini, Mahdi S.
Diffusion Generative Models (DGM) have rapidly surfaced as emerging topics in the field of computer vision, garnering significant interest across a wide array of deep learning applications. Despite their high computational demand, these models are ex
Externí odkaz:
http://arxiv.org/abs/2411.15719
Neural networks can learn spurious correlations in the data, often leading to performance disparity for underrepresented subgroups. Studies have demonstrated that the disparity is amplified when knowledge is distilled from a complex teacher model to
Externí odkaz:
http://arxiv.org/abs/2411.14984
Cardiovascular magnetic resonance (CMR) imaging is the gold standard for diagnosing several heart diseases due to its non-invasive nature and proper contrast. MR imaging is time-consuming because of signal acquisition and image formation issues. Prol
Externí odkaz:
http://arxiv.org/abs/2411.10787