Zobrazeno 1 - 10
of 7 403
pro vyhledávání: '"A. Sleiman"'
Reinforcement learning (RL) often necessitates a meticulous Markov Decision Process (MDP) design tailored to each task. This work aims to address this challenge by proposing a systematic approach to behavior synthesis and control for multi-contact lo
Externí odkaz:
http://arxiv.org/abs/2410.13817
Autor:
M. El-Azzi, A. Sleiman, A. Abou Fayad, I. Kassem, R. Bawazeer, L. Okdah, M. Doumith, M. Alghoribi, G. Matar
Publikováno v:
International Journal of Infectious Diseases, Vol 116, Iss , Pp S3- (2022)
Purpose: To investigate the acquired resistome in 18 colistin resistant Escherichia coli isolated from different poultry farms in Lebanon, analyze Inc plasmids associated with mcr, and assess potential transmission to humans. Methods & Materials: A t
Externí odkaz:
https://doaj.org/article/3861f7a7bacf4b37b2562f739cfc1bdb
Autor:
Ataallah, Kirolos, Shen, Xiaoqian, Abdelrahman, Eslam, Sleiman, Essam, Zhuge, Mingchen, Ding, Jian, Zhu, Deyao, Schmidhuber, Jürgen, Elhoseiny, Mohamed
Most current LLM-based models for video understanding can process videos within minutes. However, they struggle with lengthy videos due to challenges such as "noise and redundancy", as well as "memory and computation" constraints. In this paper, we p
Externí odkaz:
http://arxiv.org/abs/2407.12679
Green hydrogen is essential for producing renewable fuels that are needed in sectors that are hard to electrify directly. Hydrogen production in a grid-connected hybrid renewable energy plant necessitates smart planning to meet long-term hydrogen tra
Externí odkaz:
http://arxiv.org/abs/2404.11995
Autor:
Ataallah, Kirolos, Shen, Xiaoqian, Abdelrahman, Eslam, Sleiman, Essam, Zhu, Deyao, Ding, Jian, Elhoseiny, Mohamed
This paper introduces MiniGPT4-Video, a multimodal Large Language Model (LLM) designed specifically for video understanding. The model is capable of processing both temporal visual and textual data, making it adept at understanding the complexities o
Externí odkaz:
http://arxiv.org/abs/2404.03413
The emergence of differentiable simulators enabling analytic gradient computation has motivated a new wave of learning algorithms that hold the potential to significantly increase sample efficiency over traditional Reinforcement Learning (RL) methods
Externí odkaz:
http://arxiv.org/abs/2404.02887
Scenario-based testing of automated driving functions has become a promising method to reduce time and cost compared to real-world testing. In scenario-based testing automated functions are evaluated in a set of pre-defined scenarios. These scenarios
Externí odkaz:
http://arxiv.org/abs/2404.02046
Augmented Reality (AR) applications necessitates methods of inserting needed objects into scenes captured by cameras in a way that is coherent with the surroundings. Common AR applications require the insertion of predefined 3D objects with known pro
Externí odkaz:
http://arxiv.org/abs/2401.14510
Autor:
Safaoui, Sleiman, Vinod, Abraham P., Chakrabarty, Ankush, Quirynen, Rien, Yoshikawa, Nobuyuki, Di Cairano, Stefano
We consider the problem of safe multi-agent motion planning for drones in uncertain, cluttered workspaces. For this problem, we present a tractable motion planner that builds upon the strengths of reinforcement learning and constrained-control-based
Externí odkaz:
http://arxiv.org/abs/2311.00063
Recently, there has been a lot of progress in reducing the computation of deep models at inference time. These methods can reduce both the computational needs and power usage of deep models. Some of these approaches adaptively scale the compute based
Externí odkaz:
http://arxiv.org/abs/2310.02544