Zobrazeno 1 - 10
of 7 495
pro vyhledávání: '"Daoudi, A"'
Autor:
Nocentini, Federico, Besnier, Thomas, Ferrari, Claudio, Arguillere, Sylvain, Berretti, Stefano, Daoudi, Mohamed
Generating speech-driven 3D talking heads presents numerous challenges; among those is dealing with varying mesh topologies. Existing methods require a registered setting, where all meshes share a common topology: a point-wise correspondence across a
Externí odkaz:
http://arxiv.org/abs/2410.11041
Autor:
Sun, Tiezhu, Daoudi, Nadia, Kim, Kisub, Allix, Kevin, Bissyandé, Tegawendé F., Klein, Jacques
Recent advancements in ML and DL have significantly improved Android malware detection, yet many methodologies still rely on basic static analysis, bytecode, or function call graphs that often fail to capture complex malicious behaviors. DexBERT, a p
Externí odkaz:
http://arxiv.org/abs/2408.16353
Content and image generation consist in creating or generating data from noisy information by extracting specific features such as texture, edges, and other thin image structures. We are interested here in generative models, and two main problems are
Externí odkaz:
http://arxiv.org/abs/2403.14897
Autor:
Nocentini, Federico, Besnier, Thomas, Ferrari, Claudio, Arguillere, Sylvain, Berretti, Stefano, Daoudi, Mohamed
Speech-driven 3D talking heads generation has emerged as a significant area of interest among researchers, presenting numerous challenges. Existing methods are constrained by animating faces with fixed topologies, wherein point-wise correspondence is
Externí odkaz:
http://arxiv.org/abs/2403.10942
Publikováno v:
AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems
This paper addresses the problem of integrating local guide policies into a Reinforcement Learning agent. For this, we show how to adapt existing algorithms to this setting before introducing a novel algorithm based on a noisy policy-switching proced
Externí odkaz:
http://arxiv.org/abs/2402.13930
Autor:
Daoudi, Paul, Mavkov, Bojan, Robu, Bogdan, Prieur, Christophe, Witrant, Emmanuel, Barlier, Merwan, Santos, Ludovic Dos
Publikováno v:
2024 IEEE Conference on Control Technology and Applications (CCTA)
This paper presents a learning-based control strategy for non-linear throttle valves with an asymmetric hysteresis, leading to a near-optimal controller without requiring any prior knowledge about the environment. We start with a carefully tuned Prop
Externí odkaz:
http://arxiv.org/abs/2402.13654
Publikováno v:
Proceedings of the the 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024)
Off-dynamics Reinforcement Learning (ODRL) seeks to transfer a policy from a source environment to a target environment characterized by distinct yet similar dynamics. In this context, traditional RL agents depend excessively on the dynamics of the s
Externí odkaz:
http://arxiv.org/abs/2312.15474
A precondition for the deployment of a Reinforcement Learning agent to a real-world system is to provide guarantees on the learning process. While a learning algorithm will eventually converge to a good policy, there are no guarantees on the performa
Externí odkaz:
http://arxiv.org/abs/2312.15458
This paper introduces a new mathematical and numerical framework for surface analysis derived from the general setting of elastic Riemannian metrics on shape spaces. Traditionally, those metrics are defined over the infinite dimensional manifold of i
Externí odkaz:
http://arxiv.org/abs/2311.04382
Autor:
Malika Amanchar, Tarik Harit, Mounir Cherfi, Meryem Idrissi Yahyaoui, Nour Elhouda Daoudi, Abderrahmane Yahyi, Abdeslam Asehraou, Fouad Malek
Publikováno v:
Organics, Vol 5, Iss 3, Pp 290-297 (2024)
The elaboration of a new family of tetrapodal molecules L1–L3 bearing two pyrazole–tetrazole units is presented. The structure assigned to such molecules was verified by various techniques, including FTIR, NMR, HRMS and elemental analysis. The ab
Externí odkaz:
https://doaj.org/article/ddffb586e47f4543b8407e088ed7cf8c
