Zobrazeno 1 - 10
of 314
pro vyhledávání: '"Sun Mingfei"'
Autor:
Zhu Yibin, Cai Haiming, Fang Siyun, Shen Hanqin, Yan Zhuanqiang, Wang Dingai, Qi Nanshan, Li Juan, Lv Minna, Lin Xuhui, Hu Junjing, Song Yongle, Chen Xiangjie, Yin Lijun, Zhang Jianfei, Liao Shenquan, Sun Mingfei
Publikováno v:
Parasite, Vol 31, p 18 (2024)
Pentatrichomonas hominis, a flagellated parasitic protozoan, predominantly infects the mammalian digestive tract, often causing symptoms such as abdominal pain and diarrhea. However, studies investigating its pathogenicity are limited, and the mechan
Externí odkaz:
https://doaj.org/article/3474730232cf49bca31a5051d00906ba
Training large models with millions or even billions of parameters from scratch incurs substantial computational costs. Parameter Efficient Fine-Tuning (PEFT) methods, particularly Low-Rank Adaptation (LoRA), address this challenge by adapting only a
Externí odkaz:
http://arxiv.org/abs/2410.11551
An inherent fragility of quadrotor systems stems from model inaccuracies and external disturbances. These factors hinder performance and compromise the stability of the system, making precise control challenging. Existing model-based approaches eithe
Externí odkaz:
http://arxiv.org/abs/2409.11292
Feasible solutions are crucial for Integer Programming (IP) since they can substantially speed up the solving process. In many applications, similar IP instances often exhibit similar structures and shared solution distributions, which can be potenti
Externí odkaz:
http://arxiv.org/abs/2406.12349
Autor:
Lyu, Hanfang, Bai, Yuanchen, Liang, Xin, Das, Ujaan, Shi, Chuhan, Gong, Leiliang, Li, Yingchi, Sun, Mingfei, Ge, Ming, Ma, Xiaojuan
Preference-based learning aims to align robot task objectives with human values. One of the most common methods to infer human preferences is by pairwise comparisons of robot task trajectories. Traditional comparison-based preference labeling systems
Externí odkaz:
http://arxiv.org/abs/2403.06267
Robot navigation under visual corruption presents a formidable challenge. To address this, we propose a Test-time Adaptation (TTA) method, named as TTA-Nav, for point-goal navigation under visual corruptions. Our "plug-and-play" method incorporates a
Externí odkaz:
http://arxiv.org/abs/2403.01977
In this paper we explore few-shot imitation learning for control problems, which involves learning to imitate a target policy by accessing a limited set of offline rollouts. This setting has been relatively under-explored despite its relevance to rob
Externí odkaz:
http://arxiv.org/abs/2306.13554
Trust Region Policy Optimization (TRPO) is an iterative method that simultaneously maximizes a surrogate objective and enforces a trust region constraint over consecutive policies in each iteration. The combination of the surrogate objective maximiza
Externí odkaz:
http://arxiv.org/abs/2302.07985
Recent success in Deep Reinforcement Learning (DRL) methods has shown that policy optimization with respect to an off-policy distribution via importance sampling is effective for sample reuse. In this paper, we show that the use of importance samplin
Externí odkaz:
http://arxiv.org/abs/2302.02299
Autor:
Pearce, Tim, Rashid, Tabish, Kanervisto, Anssi, Bignell, Dave, Sun, Mingfei, Georgescu, Raluca, Macua, Sergio Valcarcel, Tan, Shan Zheng, Momennejad, Ida, Hofmann, Katja, Devlin, Sam
Publikováno v:
ICLR 2023
Diffusion models have emerged as powerful generative models in the text-to-image domain. This paper studies their application as observation-to-action models for imitating human behaviour in sequential environments. Human behaviour is stochastic and
Externí odkaz:
http://arxiv.org/abs/2301.10677