Zobrazeno 1 - 10
of 23 069
pro vyhledávání: '"LIU, CHEN"'
Autor:
Sun, Xingzhi, Liao, Danqi, MacDonald, Kincaid, Zhang, Yanlei, Liu, Chen, Huguet, Guillaume, Wolf, Guy, Adelstein, Ian, Rudner, Tim G. J., Krishnaswamy, Smita
Rapid growth of high-dimensional datasets in fields such as single-cell RNA sequencing and spatial genomics has led to unprecedented opportunities for scientific discovery, but it also presents unique computational and statistical challenges. Traditi
Externí odkaz:
http://arxiv.org/abs/2410.12779
Quantum-centric supercomputing presents a compelling framework for large-scale hybrid quantum-classical tasks. Although quantum machine learning (QML) offers theoretical benefits in various applications, challenges such as large-size data encoding in
Externí odkaz:
http://arxiv.org/abs/2410.09846
The rise of deepfake technologies has posed significant challenges to privacy, security, and information integrity, particularly in audio and multimedia content. This paper introduces a Quantum-Trained Convolutional Neural Network (QT-CNN) framework
Externí odkaz:
http://arxiv.org/abs/2410.09250
Autor:
Liu, Chen, Liao, Danqi, Parada-Mayorga, Alejandro, Ribeiro, Alejandro, DiStasio, Marcello, Krishnaswamy, Smita
The proliferation of digital microscopy images, driven by advances in automated whole slide scanning, presents significant opportunities for biomedical research and clinical diagnostics. However, accurately annotating densely packed information in th
Externí odkaz:
http://arxiv.org/abs/2410.03058
Autor:
Liu, Chen, Ritschel, Tobias
We propose a method to reproduce dynamic appearance textures with space-stationary but time-varying visual statistics. While most previous work decomposes dynamic textures into static appearance and motion, we focus on dynamic appearance that results
Externí odkaz:
http://arxiv.org/abs/2410.07128
Autor:
Qiu, Feng, Zhang, Wei, Liu, Chen, An, Rudong, Li, Lincheng, Ding, Yu, Fan, Changjie, Hu, Zhipeng, Yu, Xin
Video-driven 3D facial animation transfer aims to drive avatars to reproduce the expressions of actors. Existing methods have achieved remarkable results by constraining both geometric and perceptual consistency. However, geometric constraints (like
Externí odkaz:
http://arxiv.org/abs/2409.13180
This paper proposes a novel framework for large-scale scene reconstruction based on 3D Gaussian splatting (3DGS) and aims to address the scalability and accuracy challenges faced by existing methods. For tackling the scalability issue, we split the l
Externí odkaz:
http://arxiv.org/abs/2409.12774
Autor:
Sun, Xingzhi, Xu, Charles, Rocha, João F., Liu, Chen, Hollander-Bodie, Benjamin, Goldman, Laney, DiStasio, Marcello, Perlmutter, Michael, Krishnaswamy, Smita
In many data-driven applications, higher-order relationships among multiple objects are essential in capturing complex interactions. Hypergraphs, which generalize graphs by allowing edges to connect any number of nodes, provide a flexible and powerfu
Externí odkaz:
http://arxiv.org/abs/2409.09469
This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline specifically developed to address the computational challenges associated with high-dimensional multi-class neuroimaging data analysis. Standard neuroim
Externí odkaz:
http://arxiv.org/abs/2409.08584
In the Quantum-Train (QT) framework, mapping quantum state measurements to classical neural network weights is a critical challenge that affects the scalability and efficiency of hybrid quantum-classical models. The traditional QT framework employs a
Externí odkaz:
http://arxiv.org/abs/2409.06992