Zobrazeno 1 - 10
of 85
pro vyhledávání: '"Tong, Xinyi"'
Autor:
Liu, Nian, Liu, Libin, Zhang, Zilong, Wang, Zi, Xie, Hongzhao, Liu, Tengyu, Tong, Xinyi, Yang, Yaodong, He, Zhaofeng
Learning natural and diverse behaviors from human motion datasets remains challenging in physics-based character control. Existing conditional adversarial models often suffer from tight and biased embedding distributions where embeddings from the sam
Externí odkaz:
http://arxiv.org/abs/2411.06459
Designing a cost-effective sensor placement plan for sewage surveillance is a crucial task because it allows cost-effective early pandemic outbreak detection as supplementation for individual testing. However, this problem is computationally challeng
Externí odkaz:
http://arxiv.org/abs/2409.16770
This paper studies the convex hull of $d$-dimensional samples i.i.d. generated from spherically symmetric distributions. Specifically, we derive a complete integration formula for the expected facet number of the convex hull. This formula is with res
Externí odkaz:
http://arxiv.org/abs/2402.09436
Autor:
Qian, Yikai, Wang, Tianle, Tong, Xinyi, Jin, Xin, Xu, Duo, Zheng, Bo, Ge, Tiezheng, Yu, Feng, Zhu, Song-Chun
In addressing the challenge of interpretability and generalizability of artificial music intelligence, this paper introduces a novel symbolic representation that amalgamates both explicit and implicit musical information across diverse traditions and
Externí odkaz:
http://arxiv.org/abs/2401.02678
Emotion recognition in conversations (ERC) is a rapidly evolving task within the natural language processing community, which aims to detect the emotions expressed by speakers during a conversation. Recently, a growing number of ERC methods have focu
Externí odkaz:
http://arxiv.org/abs/2310.16676
Autor:
Guo, Yufei, Chen, Yuanpei, Zhang, Liwen, Liu, Xiaode, Tong, Xinyi, Ou, Yuanyuan, Huang, Xuhui, Ma, Zhe
The Spiking Neural Network (SNN) has attracted more and more attention recently. It adopts binary spike signals to transmit information. Benefitting from the information passing paradigm of SNNs, the multiplications of activations and weights can be
Externí odkaz:
http://arxiv.org/abs/2307.04356
Data heterogeneity is one of the most challenging issues in federated learning, which motivates a variety of approaches to learn personalized models for participating clients. One such approach in deep neural networks based tasks is employing a share
Externí odkaz:
http://arxiv.org/abs/2306.11867
Autor:
Guo, Yufei, Zhang, Liwen, Chen, Yuanpei, Tong, Xinyi, Liu, Xiaode, Wang, YingLei, Huang, Xuhui, Ma, Zhe
Brain-inspired spiking neural networks (SNNs) have recently drawn more and more attention due to their event-driven and energy-efficient characteristics. The integration of storage and computation paradigm on neuromorphic hardwares makes SNNs much di
Externí odkaz:
http://arxiv.org/abs/2210.06686
In this paper, we study the information transmission problem under the distributed learning framework, where each worker node is merely permitted to transmit a $m$-dimensional statistic to improve learning results of the target node. Specifically, we
Externí odkaz:
http://arxiv.org/abs/2205.06515
Efficient and green production of flavone-5-O-glycosides by glycosyltransferases in Escherichia coli
Autor:
Jia, Shutong, Lu, Changning, Tong, Xinyi, Li, Qi, Yan, Siyang, Pei, Jianjun, Dai, Yuan, Zhao, Linguo
Publikováno v:
In International Journal of Biological Macromolecules October 2024 277 Part 4
