Zobrazeno 1 - 10
of 20 454
pro vyhledávání: '"Diversity of learning"'
Learning diverse and high-performance behaviors from a limited set of demonstrations is a grand challenge. Traditional imitation learning methods usually fail in this task because most of them are designed to learn one specific behavior even with mul
Externí odkaz:
http://arxiv.org/abs/2411.06965
Autor:
Wan, Zhenglin, Yu, Xingrui, Bossens, David Mark, Lyu, Yueming, Guo, Qing, Fan, Flint Xiaofeng, Tsang, Ivor
Imitation learning (IL) has shown great potential in various applications, such as robot control. However, traditional IL methods are usually designed to learn only one specific type of behavior since demonstrations typically correspond to a single e
Externí odkaz:
http://arxiv.org/abs/2410.06151
Although instruction tuning is widely used to adjust behavior in Large Language Models (LLMs), extensive empirical evidence and research indicates that it is primarily a process where the model fits to specific task formats, rather than acquiring new
Externí odkaz:
http://arxiv.org/abs/2408.10841
The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning
Autor:
Wu, Xinghao, Liu, Xuefeng, Niu, Jianwei, Zhu, Guogang, Tang, Shaojie, Li, Xiaotian, Cao, Jiannong
Personalized Federated Learning (PFL) is a commonly used framework that allows clients to collaboratively train their personalized models. PFL is particularly useful for handling situations where data from different clients are not independent and id
Externí odkaz:
http://arxiv.org/abs/2407.15464
Autor:
Ishmail, Habiba1 (AUTHOR) habdoolkhader@gmail.com, Ngene, Nnabuike Chibuoke2,3 (AUTHOR)
Publikováno v:
Journal of Medical Education & Curricular Development. 12/10/2024, p1-4. 4p.
Autor:
Ruffin, Makai A.1 (AUTHOR) mar24@rice.edu, Tudor, Ryann N.1 (AUTHOR), Beier, Margaret E.1 (AUTHOR)
Publikováno v:
Behavioral Sciences (2076-328X). Sep2024, Vol. 14 Issue 9, p764. 18p.
Autor:
Wang, Zihan, Xiao, Jiayu, Li, Mengxiang, He, Zhongjiang, Li, Yongxiang, Wang, Chao, Song, Shuangyong
In our dynamic world where data arrives in a continuous stream, continual learning enables us to incrementally add new tasks/domains without the need to retrain from scratch. A major challenge in continual learning of language model is catastrophic f
Externí odkaz:
http://arxiv.org/abs/2403.10894
Autor:
Dennis Conrad
Caribbean Discourse in Inclusive Education Volume II “Responding to Learner Diversity and Learner Difficulties” shares selected critical reflections and recommendations on the way educational communities respond to student diversity and difficult
Autor:
Batra, Sumeet, Tjanaka, Bryon, Fontaine, Matthew C., Petrenko, Aleksei, Nikolaidis, Stefanos, Sukhatme, Gaurav
Training generally capable agents that thoroughly explore their environment and learn new and diverse skills is a long-term goal of robot learning. Quality Diversity Reinforcement Learning (QD-RL) is an emerging research area that blends the best asp
Externí odkaz:
http://arxiv.org/abs/2305.13795