Zobrazeno 1 - 10
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pro vyhledávání: '"Pham, Tuan"'
How is the irreversibility of a high-dimensional chaotic system controlled by the heterogeneity in the non-reciprocal interactions among its elements? In this paper, we address this question using a stochastic model of random recurrent neural network
Externí odkaz:
http://arxiv.org/abs/2407.17939
We present Piva (Preserving Identity with Variational Score Distillation), a novel optimization-based method for editing images and 3D models based on diffusion models. Specifically, our approach is inspired by the recently proposed method for 2D ima
Externí odkaz:
http://arxiv.org/abs/2406.08953
Autor:
Pham, Tuan, Mandt, Stephan
Neural Radiance Fields (NeRFs) have emerged as powerful tools for capturing detailed 3D scenes through continuous volumetric representations. Recent NeRFs utilize feature grids to improve rendering quality and speed; however, these representations in
Externí odkaz:
http://arxiv.org/abs/2406.08943
Autor:
Berrens, Margaret, Kundu, Arpan, Andrade, Marcos F. Calegari, Pham, Tuan Anh, Galli, Giulia, Donadio, Davide
The electronic properties and optical response of ice and water are intricately shaped by their molecular structure, including the quantum mechanical nature of hydrogen atoms. In spite of numerous studies appeared over decades, a comprehensive unders
Externí odkaz:
http://arxiv.org/abs/2405.06207
We derive exact equations for the spectral density of sparse networks with an arbitrary distribution of the number of single edges and triangles per node. These equations enable a systematic investigation of the effect of clustering on the spectral p
Externí odkaz:
http://arxiv.org/abs/2404.08152
In this work, we demonstrate the formation and electronic influence of lateral heterointerfaces in FeSn containing Kagome and honeycomb layers. Lateral heterostructures offer spatially resolved property control, enabling the integration of dissimilar
Externí odkaz:
http://arxiv.org/abs/2403.07278
In this work, we have proposed an approach for improving the GCN for predicting ratings in social networks. Our model is expanded from the standard model with several layers of transformer architecture. The main focus of the paper is on the encoder a
Externí odkaz:
http://arxiv.org/abs/2401.06436
Autor:
Kwon, Hyuna, Hsu, Tim, Sun, Wenyu, Jeong, Wonseok, Aydin, Fikret, Chapman, James, Chen, Xiao, Carbone, Matthew R., Lu, Deyu, Zhou, Fei, Pham, Tuan Anh
The ability to rapidly develop materials with desired properties has a transformative impact on a broad range of emerging technologies. In this work, we introduce a new framework based on the diffusion model, a recent generative machine learning meth
Externí odkaz:
http://arxiv.org/abs/2312.05472
Autor:
Pham, Tuan Minh, Ju, Xiangyang
Accurate simulation of detector responses to hadrons is paramount for all physics programs at the Large Hadron Collider (LHC). Central to this simulation is the modeling of hadronic interactions. Unfortunately, the absence of first-principle theoreti
Externí odkaz:
http://arxiv.org/abs/2310.07553