Meta-featuresDeep setsDeep learningHyperparameter optimizationMeta-learning, or learning to learn, is a machine learning approach that utilizes prior learning experiences to expedite the learning process on unseen tasks. As a data-driven approach, meta-learning requires meta-features that represent the ...
1, …,mj,K) ofKmeta-featuresmj,k ∈ M, the set of all known meta-features. This can be used to define a task similarity measure based on, for instance, the Euclidean distance betweenm(ti) andm(tj),
Transfer learning works well when the features that are automatically extracted by the network from the input images are useful across multiple related tasks, such as the abstract features extracted from common objects in photographs.This is typically understood in a supervised learning context, where ...
Meta Learning，叫做元学习或者 Learning to Learn 学会学习，包括Zero-Shot/One-Shot/Few-Shot 学习，...
Code Issues Pull requests Python Meta-Feature Extractor package. machine-learning automl meta-learning meta-features metalearning meta-feature Updated Jun 22, 2024 Python HLTCHKUST / PAML Star 128 Code Issues Pull requests Personalizing Dialogue Agents via Meta-Learning transformer chat-bot maml...
Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach. Zhenjie Zhao, Xiaojuan Ma. EMNLP 2019. [pdf] [code](metric-based) Machine Translation Meta-Learning for Low-Resource Neural Machine Translation. Jiatao Gu, Yong Wang, Yun Chen, Victor O. K. Li, Kyunghyun Cho. EM...
An extensive analysis of different categories of meta-features, meta-learners, and setups across 156 datasets is performed. Results show that it is possible to accurately predict when tuning will significantly improve the performance of the induced models. The proposed system reduces the time spent ...
Moreover, to develop AI-assisted CT diagnostic technology for the classification of pulmonary nodules as benign or malignant, data related to the texture features extracted from CT images are usually divided into a training dataset and a testing dataset. The training dataset is used to develop a ...
Machine LearningGopal S, Yang Y. Multilabel classification with meta-level features. In: Proceedings of the 33rd interna- tional ACM SIGIR conference on Research and development in information retrieval. ACM; 2010. p. 315-322.S. Gopal and Y. Yang, "Multilabel classification with meta- level...
Meta-features 1Introduction For humans, experiences from the past are usually helpful when learning a new skill or solving a new problem. Equivalently, in the context of machine learning, meta-learning takes advantage of prior experience acquired when solving related tasks for approaching new problems...