Zobrazeno 1 - 10
of 4 837
pro vyhledávání: '"Sili A"'
Publikováno v:
Jurnal Bisnis dan Kewirausahaan, Vol 18, Iss 2, Pp 94-101 (2022)
Penelitian ini bertujuan untuk mengetahui pengaruh entrepreneurial marketing terhadap inovasi produk pada UMKM di Desa Petak Gianyar Bali; untuk mengetahui pengaruh inovasi produk terhadap daya saing; untuk mengetahui pengaruh entreprenurial marketin
Externí odkaz:
https://doaj.org/article/9ddb39af6c2b4ab1afad74ae54a30b5e
Autor:
Zhao, Wang, Liu, Jiachen, Zhang, Sheng, Li, Yishu, Chen, Sili, Huang, Sharon X, Liu, Yong-Jin, Guo, Hengkai
This paper presents a generalizable 3D plane detection and reconstruction framework named MonoPlane. Unlike previous robust estimator-based works (which require multiple images or RGB-D input) and learning-based works (which suffer from domain shift)
Externí odkaz:
http://arxiv.org/abs/2411.01226
Autor:
Hu, Jifeng, Huang, Sili, Shen, Li, Yang, Zhejian, Hu, Shengchao, Tang, Shisong, Chen, Hechang, Chang, Yi, Tao, Dacheng, Sun, Lichao
Continual offline reinforcement learning (CORL) has shown impressive ability in diffusion-based lifelong learning systems by modeling the joint distributions of trajectories. However, most research only focuses on limited continual task settings wher
Externí odkaz:
http://arxiv.org/abs/2410.15698
Autor:
Hu, Jifeng, Shen, Li, Huang, Sili, Yang, Zhejian, Chen, Hechang, Sun, Lichao, Chang, Yi, Tao, Dacheng
Artificial neural networks, especially recent diffusion-based models, have shown remarkable superiority in gaming, control, and QA systems, where the training tasks' datasets are usually static. However, in real-world applications, such as robotic co
Externí odkaz:
http://arxiv.org/abs/2409.02512
Publikováno v:
Computer Methods in Applied Mechanics and Engineering 432 (2024) 117397
Kolmogorov-Arnold networks (KANs) as an alternative to multi-layer perceptrons (MLPs) are a recent development demonstrating strong potential for data-driven modeling. This work applies KANs as the backbone of a neural ordinary differential equation
Externí odkaz:
http://arxiv.org/abs/2407.04192
Autor:
Huang, Sili, Hu, Jifeng, Yang, Zhejian, Yang, Liwei, Luo, Tao, Chen, Hechang, Sun, Lichao, Yang, Bo
Recent works have shown the remarkable superiority of transformer models in reinforcement learning (RL), where the decision-making problem is formulated as sequential generation. Transformer-based agents could emerge with self-improvement in online e
Externí odkaz:
http://arxiv.org/abs/2406.00079
In-context learning is a promising approach for offline reinforcement learning (RL) to handle online tasks, which can be achieved by providing task prompts. Recent works demonstrated that in-context RL could emerge with self-improvement in a trial-an
Externí odkaz:
http://arxiv.org/abs/2405.20692
Autor:
Li, Zijian, Cai, Ruichu, Huang, Haiqin, Zhang, Sili, Yan, Yuguang, Hao, Zhifeng, Dong, Zhenghua
Existing model-based interactive recommendation systems are trained by querying a world model to capture the user preference, but learning the world model from historical logged data will easily suffer from bias issues such as popularity bias and sam
Externí odkaz:
http://arxiv.org/abs/2402.15819
At the fundamental level, full description of light-matter interaction requires quantum treatment of both matter and light. However, for standard light sources generating intense laser pulses carrying quadrillions of photons in a coherent state, clas
Externí odkaz:
http://arxiv.org/abs/2401.02817
Publikováno v:
Leadership in Health Services, 2024, Vol. 37, Issue 4, pp. 526-547.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/LHS-12-2023-0099