Zobrazeno 1 - 10
of 46 855
pro vyhledávání: '"Meta learning"'
Autor:
Gharoun, Hassan1 (AUTHOR) hassan.gharoun@student.uts.edu.au, Momenifar, Fereshteh2 (AUTHOR) 22046851@student.westernsydney.edu.au, Chen, Fang1 (AUTHOR) fang.chen@uts.edu.au, Gandomi, Amir3 (AUTHOR) gandomi@uts.edu.au
Publikováno v:
ACM Computing Surveys. Dec2024, Vol. 56 Issue 12, p1-41. 41p.
Autor:
Jučas, Augustinas, Raman, Chirag
Adaptively forecasting human behavior in social settings is an important step toward achieving Artificial General Intelligence. Most existing research in social forecasting has focused either on unfocused interactions, such as pedestrian trajectory p
Externí odkaz:
http://arxiv.org/abs/2501.01915
Meta-learning, i.e., "learning to learn", is a promising approach to enable efficient BCI classifier training with limited amounts of data. It can effectively use collections of in some way similar classification tasks, with rapid adaptation to new t
Externí odkaz:
http://arxiv.org/abs/2412.19725
Autor:
Feng, Pengxing, So, Hing Cheung
Active noise control typically employs adaptive filtering to generate secondary noise, where the least mean square algorithm is the most widely used. However, traditional updating rules are linear and exhibit limited effectiveness in addressing nonli
Externí odkaz:
http://arxiv.org/abs/2412.19471
Dynamic Music Emotion Recognition (DMER) aims to predict the emotion of different moments in music, playing a crucial role in music information retrieval. The existing DMER methods struggle to capture long-term dependencies when dealing with sequence
Externí odkaz:
http://arxiv.org/abs/2412.19200
Discovering a unique causal structure is difficult due to both inherent identifiability issues, and the consequences of finite data. As such, uncertainty over causal structures, such as those obtained from a Bayesian posterior, are often necessary fo
Externí odkaz:
http://arxiv.org/abs/2412.16577
The optimization-based meta-learning approach is gaining increased traction because of its unique ability to quickly adapt to a new task using only small amounts of data. However, existing optimization-based meta-learning approaches, such as MAML, AN
Externí odkaz:
http://arxiv.org/abs/2412.12030