Zobrazeno 1 - 10
of 10 926
pro vyhledávání: '"Jinzhu"'
Data augmentation creates new data points by transforming the original ones for a reinforcement learning (RL) agent to learn from, which has been shown to be effective for the objective of improving the data efficiency of RL for continuous control. P
Externí odkaz:
http://arxiv.org/abs/2410.12983
Autor:
Gao, Chen, Zhao, Baining, Zhang, Weichen, Mao, Jinzhu, Zhang, Jun, Zheng, Zhiheng, Man, Fanhang, Fang, Jianjie, Zhou, Zile, Cui, Jinqiang, Chen, Xinlei, Li, Yong
Embodied artificial intelligence emphasizes the role of an agent's body in generating human-like behaviors. The recent efforts on EmbodiedAI pay a lot of attention to building up machine learning models to possess perceiving, planning, and acting abi
Externí odkaz:
http://arxiv.org/abs/2410.09604
Mix-up is a key technique for consistency regularization-based semi-supervised learning methods, generating strong-perturbed samples for strong-weak pseudo-supervision. Existing mix-up operations are performed either randomly or with predefined rules
Externí odkaz:
http://arxiv.org/abs/2407.21586
The existing barely-supervised medical image segmentation (BSS) methods, adopting a registration-segmentation paradigm, aim to learn from data with very few annotations to mitigate the extreme label scarcity problem. However, this paradigm poses a ch
Externí odkaz:
http://arxiv.org/abs/2407.05248
Autor:
Wang, Xiaofei, Eskimez, Sefik Emre, Thakker, Manthan, Yang, Hemin, Zhu, Zirun, Tang, Min, Xia, Yufei, Li, Jinzhu, Zhao, Sheng, Li, Jinyu, Kanda, Naoyuki
Recently, zero-shot text-to-speech (TTS) systems, capable of synthesizing any speaker's voice from a short audio prompt, have made rapid advancements. However, the quality of the generated speech significantly deteriorates when the audio prompt conta
Externí odkaz:
http://arxiv.org/abs/2406.05699
This paper investigates an extremely challenging problem: barely-supervised volumetric medical image segmentation (BSS). A BSS training dataset consists of two parts: 1) a barely-annotated labeled set, where each labeled image contains only a single-
Externí odkaz:
http://arxiv.org/abs/2405.09777
Integrated sensing and communication (ISAC) is expected to play a crucial role in the sixth-generation (6G) mobile communication systems, offering potential applications in the scenarios of intelligent transportation, smart factories, etc. The perfor
Externí odkaz:
http://arxiv.org/abs/2404.17462
Autor:
Hou, Qingshan, Cheng, Shuai, Cao, Peng, Yang, Jinzhu, Liu, Xiaoli, Zaiane, Osmar R., Tham, Yih Chung
Representation learning offers a conduit to elucidate distinctive features within the latent space and interpret the deep models. However, the randomness of lesion distribution and the complexity of low-quality factors in medical images pose great ch
Externí odkaz:
http://arxiv.org/abs/2404.04887
The emerging programmable networks sparked significant research on Intelligent Network Data Plane (INDP), which achieves learning-based traffic analysis at line-speed. Prior art in INDP focus on deploying tree/forest models on the data plane. We obse
Externí odkaz:
http://arxiv.org/abs/2403.11090
Identifying critical nodes in networks is a classical decision-making task, and many methods struggle to strike a balance between adaptability and utility. Therefore, we propose an approach that empowers Evolutionary Algorithm (EA) with Large Languag
Externí odkaz:
http://arxiv.org/abs/2403.03962