Zobrazeno 1 - 10
of 31 477
pro vyhledávání: '"Bogdán, Á."'
Solving systems of partial differential equations (PDEs) is a fundamental task in computational science, traditionally addressed by numerical solvers. Recent advancements have introduced neural operators and physics-informed neural networks (PINNs) t
Externí odkaz:
http://arxiv.org/abs/2411.16663
The growing demand for customized visual content has led to the rise of personalized text-to-image (T2I) diffusion models. Despite their remarkable potential, they pose significant privacy risk when misused for malicious purposes. In this paper, we p
Externí odkaz:
http://arxiv.org/abs/2411.16437
This paper introduces a novel Laplacian matrix aiming to enable the construction of spectral convolutional networks and to extend the signal processing applications for directed graphs. Our proposal is inspired by a Haar-like transformation and produ
Externí odkaz:
http://arxiv.org/abs/2411.15527
Autor:
Karpov, Artem, Cho, Seong Hah, Meek, Austin, Koopmanschap, Raymond, Farnik, Lucy, Cirstea, Bogdan-Ionut
In this work, we study the alignment (BrainScore) of large language models (LLMs) fine-tuned for moral reasoning on behavioral data and/or brain data of humans performing the same task. We also explore if fine-tuning several LLMs on the fMRI data of
Externí odkaz:
http://arxiv.org/abs/2411.15386
Accurate lower-limb joint kinematic estimation is critical for applications such as patient monitoring, rehabilitation, and exoskeleton control. While previous studies have employed wearable sensor-based deep learning (DL) models for estimating joint
Externí odkaz:
http://arxiv.org/abs/2411.15366
Autor:
Popescu, Mihail N., Nicola, Bogdan Adrian, Uspal, William E., Domínguez, Alvaro, Gáspár, Szilveszter
Patches of catalyst imprinted on supporting walls induce motion of the fluid around them once they are supplied with the chemical species (``fuel'') that are converted by the catalytic chemical reaction. While the functioning of such chemically activ
Externí odkaz:
http://arxiv.org/abs/2411.12107
Many practical prediction algorithms represent inputs in Euclidean space and replace the discrete 0/1 classification loss with a real-valued surrogate loss, effectively reducing classification tasks to stochastic optimization. In this paper, we inves
Externí odkaz:
http://arxiv.org/abs/2411.10784
Autor:
Xiao, Xiongye, Li, Shixuan, Huang, Luzhe, Liu, Gengshuo, Nguyen, Trung-Kien, Huang, Yi, Chang, Di, Kochenderfer, Mykel J., Bogdan, Paul
While working within the spatial domain can pose problems associated with ill-conditioned scores caused by power-law decay, recent advances in diffusion-based generative models have shown that transitioning to the wavelet domain offers a promising al
Externí odkaz:
http://arxiv.org/abs/2411.09356
The paper presents a Graph Attention Convolutional Network (GACN) for flow reconstruction from very sparse data in time-varying geometries. The model incorporates a feature propagation algorithm as a preprocessing step to handle extremely sparse inpu
Externí odkaz:
http://arxiv.org/abs/2411.08764
We construct a strong Markov process corresponding to the Dirichlet form of Servadei and Valdinoci and use the process to solve the corresponding Neumann boundary problem for the fractional Laplacian and the half-line.
Comment: 85 pages
Comment: 85 pages
Externí odkaz:
http://arxiv.org/abs/2411.08220