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pro vyhledávání: '"Omar, Marwan"'
The rising use of Large Language Models (LLMs) to create and disseminate malware poses a significant cybersecurity challenge due to their ability to generate and distribute attacks with ease. A single prompt can initiate a wide array of malicious act
Externí odkaz:
http://arxiv.org/abs/2409.07587
Autor:
Omar, Marwan
In recent years, the convergence of cybersecurity, artificial intelligence (AI), and data management has emerged as a critical area of research, driven by the increasing complexity and interdependence of modern technological ecosystems. This paper pr
Externí odkaz:
http://arxiv.org/abs/2408.05888
Autor:
Gholami, Sia, Omar, Marwan
Natural Language Processing (NLP) has undergone transformative changes with the advent of deep learning methodologies. One challenge persistently confronting researchers is the scarcity of high-quality, annotated datasets that drive these models. Thi
Externí odkaz:
http://arxiv.org/abs/2310.07830
Autor:
Gholami, Sia, Omar, Marwan
Transformer models have revolutionized natural language processing with their unparalleled ability to grasp complex contextual relationships. However, the vast number of parameters in these models has raised concerns regarding computational efficienc
Externí odkaz:
http://arxiv.org/abs/2310.04573
Autor:
Gholami, Sia, Omar, Marwan
The burgeoning complexity of contemporary deep learning models, while achieving unparalleled accuracy, has inadvertently introduced deployment challenges in resource-constrained environments. Knowledge distillation, a technique aiming to transfer kno
Externí odkaz:
http://arxiv.org/abs/2310.02421
Autor:
Gholami, Sia, Omar, Marwan
This paper presents novel systems and methodologies for the development of efficient large language models (LLMs). It explores the trade-offs between model size, performance, and computational resources, with the aim of maximizing the efficiency of t
Externí odkaz:
http://arxiv.org/abs/2309.06589
Autor:
Omar, Marwan
Recently, deep learning techniques have garnered substantial attention for their ability to identify vulnerable code patterns accurately. However, current state-of-the-art deep learning models, such as Convolutional Neural Networks (CNN), and Long Sh
Externí odkaz:
http://arxiv.org/abs/2302.11773
Autor:
Omar, Marwan
As machine learning (ML) systems are being increasingly employed in the real world to handle sensitive tasks and make decisions in various fields, the security and privacy of those models have also become increasingly critical. In particular, Deep Ne
Externí odkaz:
http://arxiv.org/abs/2302.09420
Autor:
Omar, Marwan
Although backdoor learning is an active research topic in the NLP domain, the literature lacks studies that systematically categorize and summarize backdoor attacks and defenses. To bridge the gap, we present a comprehensive and unifying study of bac
Externí odkaz:
http://arxiv.org/abs/2302.06801