Zobrazeno 1 - 10
of 354
pro vyhledávání: '"Liu Zihe"'
Model Compression has drawn much attention within the deep learning community recently. Compressing a dense neural network offers many advantages including lower computation cost, deployability to devices of limited storage and memories, and resistan
Externí odkaz:
http://arxiv.org/abs/2411.00273
This study introduces Variational Automatic Relevance Determination (VARD), a novel approach tailored for fitting sparse additive regression models in high-dimensional settings. VARD distinguishes itself by its ability to independently assess the smo
Externí odkaz:
http://arxiv.org/abs/2411.00256
Chain-of-thought prompting significantly boosts the reasoning ability of large language models but still faces three issues: hallucination problem, restricted interpretability, and uncontrollable generation. To address these challenges, we present Ag
Externí odkaz:
http://arxiv.org/abs/2409.12411
The number of free parameters, or dimension, of a model is a straightforward way to measure its complexity: a model with more parameters can encode more information. However, this is not an accurate measure of complexity: models capable of memorizing
Externí odkaz:
http://arxiv.org/abs/2409.04913
Deep reinforcement learning is used in various domains, but usually under the assumption that the environment has stationary conditions like transitions and state distributions. When this assumption is not met, performance suffers. For this reason, t
Externí odkaz:
http://arxiv.org/abs/2405.14214
The progress of EEG-based emotion recognition has received widespread attention from the fields of human-machine interactions and cognitive science in recent years. However, how to recognize emotions with limited labels has become a new research and
Externí odkaz:
http://arxiv.org/abs/2208.00877
Publikováno v:
In Computers and Electronics in Agriculture December 2024 227 Part 2
Publikováno v:
In International Journal of Biological Macromolecules December 2024 282 Part 6
Autor:
Wang, Kai, Su, Changsheng, Bi, Haoran, Zhang, Changwei, Cai, Di, Liu, Yanhui, Wang, Meng, Chen, Biqiang, Nielsen, Jens, Liu, Zihe, Tan, Tianwei
Publikováno v:
In Green Energy & Environment November 2024 9(11):1759-1770
Publikováno v:
In Journal of Hydrology November 2024 644