Zobrazeno 1 - 10
of 78
pro vyhledávání: '"Bo Yu"'
Text-to-music models allow users to generate nearly realistic musical audio with textual commands. However, editing music audios remains challenging due to the conflicting desiderata of performing fine-grained alterations on the audio while maintaini
Externí odkaz:
http://arxiv.org/abs/2407.16564
We present a nonparametric construction for deep learning compatible modern Hopfield models and utilize this framework to debut an efficient variant. Our key contribution stems from interpreting the memory storage and retrieval processes in modern Ho
Externí odkaz:
http://arxiv.org/abs/2404.03900
In this work, maximal $\alpha$-leakage is introduced to quantify how much a quantum adversary can learn about any sensitive information of data upon observing its disturbed version via a quantum privacy mechanism. We first show that an adversary's ma
Externí odkaz:
http://arxiv.org/abs/2403.14450
In this paper, we propose an algorithm that allows joint refinement of camera pose and scene geometry represented by decomposed low-rank tensor, using only 2D images as supervision. First, we conduct a pilot study based on a 1D signal and relate our
Externí odkaz:
http://arxiv.org/abs/2402.13252
We present STanHop-Net (Sparse Tandem Hopfield Network) for multivariate time series prediction with memory-enhanced capabilities. At the heart of our approach is STanHop, a novel Hopfield-based neural network block, which sparsely learns and stores
Externí odkaz:
http://arxiv.org/abs/2312.17346
We introduce the sparse modern Hopfield model as a sparse extension of the modern Hopfield model. Like its dense counterpart, the sparse modern Hopfield model equips a memory-retrieval dynamics whose one-step approximation corresponds to the sparse a
Externí odkaz:
http://arxiv.org/abs/2309.12673
In this research, we propose a novel technique for visualizing nonstationarity in geostatistics, particularly when confronted with a single realization of data at irregularly spaced locations. Our method hinges on formulating a statistic that tracks
Externí odkaz:
http://arxiv.org/abs/2210.08231
While generative adversarial networks (GANs) have been widely used in research on audio generation, the training of a GAN model is known to be unstable, time consuming, and data inefficient. Among the attempts to ameliorate the training process of GA
Externí odkaz:
http://arxiv.org/abs/2209.01751
Autor:
Wei, Shi-Hai, Jing, Bo, Zhang, Xue-Ying, Liao, Jin-Yu, Yuan, Chen-Zhi, Fan, Bo-Yu, Lyu, Chen, Zhou, Dian-Li, Wang, You, Deng, Guang-Wei, Song, Hai-Zhi, Oblak, Daniel, Guo, Guang-Can, Zhou, Qiang
Publikováno v:
Laser & Photonics Reviews volume 16, Article number: 2100219 (2022)
Quantum networks play an extremely important role in quantum information science, with application to quantum communication, computation, metrology and fundamental tests. One of the key challenges for implementing a quantum network is to distribute e
Externí odkaz:
http://arxiv.org/abs/2201.04802
Autor:
Xu, Bo-Yu, Chen, Li-Kun, Lin, Jintian, Feng1, Lan-Tian, Niu, Rui, Zhou, Zhi-Yuan, Gao, Renhong, Dong, Chun-Hua, Guo, Guang-Can, Gong, Qihuang, Cheng, Ya, Xiao, Yun-Feng, Ren, Xi-Feng
On-chip bright quantum sources with multiplexing ability are extremely high in demand for the integrated quantum networks with unprecedented scalability and complexity. Here, we demonstrate an ultrabright and broadband biphoton quantum source generat
Externí odkaz:
http://arxiv.org/abs/2110.08997