Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Tu, Zhuozhuo"'
Entanglement serves as the resource to empower quantum computing. Recent progress has highlighted its positive impact on learning quantum dynamics, wherein the integration of entanglement into quantum operations or measurements of quantum machine lea
Externí odkaz:
http://arxiv.org/abs/2306.03481
The intrinsic probabilistic nature of quantum mechanics invokes endeavors of designing quantum generative learning models (QGLMs). Despite the empirical achievements, the foundations and the potential advantages of QGLMs remain largely obscure. To na
Externí odkaz:
http://arxiv.org/abs/2205.04730
Deep neural networks have achieved impressive performance in a variety of tasks over the last decade, such as autonomous driving, face recognition, and medical diagnosis. However, prior works show that deep neural networks are easily manipulated into
Externí odkaz:
http://arxiv.org/abs/2112.14889
Autor:
Lei, Shiye, Tu, Zhuozhuo, Rutkowski, Leszek, Zhou, Feng, Shen, Li, He, Fengxiang, Tao, Dacheng
Bayesian neural networks (BNNs) have become a principal approach to alleviate overconfident predictions in deep learning, but they often suffer from scaling issues due to a large number of distribution parameters. In this paper, we discover that the
Externí odkaz:
http://arxiv.org/abs/2112.06281
Publikováno v:
Phys. Rev. Lett. 128, 080506 (2022)
The superiority of variational quantum algorithms (VQAs) such as quantum neural networks (QNNs) and variational quantum eigen-solvers (VQEs) heavily depends on the expressivity of the employed ansatze. Namely, a simple ansatze is insufficient to capt
Externí odkaz:
http://arxiv.org/abs/2104.09961
Differentiable neural architecture search (DARTS) has gained much success in discovering flexible and diverse cell types. To reduce the evaluation gap, the supernet is expected to have identical layers with the target network. However, even for this
Externí odkaz:
http://arxiv.org/abs/2011.09300
Detecting abnormal connectivity in schizophrenia via a joint directed acyclic graph estimation model
Functional connectivity (FC) has been widely used to study brain network interactions underlying the emerging cognition and behavior of an individual. FC is usually defined as the correlation or partial correlation between brain regions. Although FC
Externí odkaz:
http://arxiv.org/abs/2010.13029
Here we propose a general theoretical method for analyzing the risk bound in the presence of adversaries. Specifically, we try to fit the adversarial learning problem into the minimax framework. We first show that the original adversarial learning pr
Externí odkaz:
http://arxiv.org/abs/1811.05232
Detecting abnormal connectivity in schizophrenia via a joint directed acyclic graph estimation model
Autor:
Zhang, Gemeng a, Cai, Biao a, Zhang, Aiying b, Tu, Zhuozhuo c, Xiao, Li d, Stephen, Julia M. e, Wilson, Tony W. f, Calhoun, Vince D. g, Wang, Yu-Ping ⁎, a
Publikováno v:
In NeuroImage 15 October 2022 260
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