Zobrazeno 1 - 10
of 13 143
pro vyhledávání: '"Luqman, A."'
Autor:
Alyami, Sarah, Luqman, Hamzah
Continuous Sign Language Recognition (CSLR) focuses on the interpretation of a sequence of sign language gestures performed continually without pauses. In this study, we conduct an empirical evaluation of recent deep learning CSLR techniques and asse
Externí odkaz:
http://arxiv.org/abs/2406.12369
The promise and proliferation of large-scale dynamic federated learning gives rise to a prominent open question - is it prudent to share data or model across nodes, if efficiency of transmission and fast knowledge transfer are the prime objectives. T
Externí odkaz:
http://arxiv.org/abs/2406.10798
Advances in deepfake research have led to the creation of almost perfect manipulations undetectable by human eyes and some deepfakes detection tools. Recently, several techniques have been proposed to differentiate deepfakes from realistic images and
Externí odkaz:
http://arxiv.org/abs/2406.08625
This work integrates peer-to-peer federated learning tools with NS3, a widely used network simulator, to create a novel simulator designed to allow heterogeneous device experiments in federated learning. This cross-platform adaptability addresses a c
Externí odkaz:
http://arxiv.org/abs/2405.17839
This paper details the privacy and security landscape in today's cloud ecosystem and identifies that there is a gap in addressing the risks introduced by machine learning models. As machine learning algorithms continue to evolve and find applications
Externí odkaz:
http://arxiv.org/abs/2402.00896
We establish a comprehensive theoretical framework for systems subjected to a static uniform temperature gradient, employing the non-equilibrium Keldysh-Dyson formalism. This framework interprets the statistical force due to the temperature gradient
Externí odkaz:
http://arxiv.org/abs/2401.17652
Due to the low dimensionality in the quantization of the electronic states and degree of freedom for device modulation, two-dimensional (2D) ferromagnetism plays a critical role in lots of fields. In this study, we perform first-principles calculatio
Externí odkaz:
http://arxiv.org/abs/2401.14035
Realizing the recent advances in Natural Language Processing (NLP) to the legal sector poses challenging problems such as extremely long sequence lengths, specialized vocabulary that is usually only understood by legal professionals, and high amounts
Externí odkaz:
http://arxiv.org/abs/2311.08890
Autor:
Alyafeai, Zaid, Alshaibani, Maged S., AlKhamissi, Badr, Luqman, Hamzah, Alareqi, Ebrahim, Fadel, Ali
Large language models (LLMs) have demonstrated impressive performance on various downstream tasks without requiring fine-tuning, including ChatGPT, a chat-based model built on top of LLMs such as GPT-3.5 and GPT-4. Despite having a lower training pro
Externí odkaz:
http://arxiv.org/abs/2306.16322
The use of deep unfolding networks in compressive sensing (CS) has seen wide success as they provide both simplicity and interpretability. However, since most deep unfolding networks are iterative, this incurs significant redundancies in the network.
Externí odkaz:
http://arxiv.org/abs/2305.05505