Meta-learning, also known as “learning to learn”, intends to design models that can learn new skills or adapt to new environments rapidly with a few training examples. There are three common approaches: 1) learn an efficient distance metric (metric-based); 2) use (recurrent) network with ...
Meta-learningLearning to learnApplicationCompared to traditional machine learning, deep learning can learn deeper abstract data representation and understand scattered data properties. It has gained considerable attention for its extraordinary performances. However, existing deep learning algorithms perform poorly...
The problem with existing meta-learning approaches is thatthe initial model can be trained biased towards some tasks, particularly those sampled in meta-training phase.Such a biased initial model may not be well generalizable to an unseen task that has a large deviation from meta-training tasks, ...
无非就是meta-learning-based approaches显式得从support feature转换过来，而finetune-based则是learnable ...
This method exhibited superior performance compared to traditional fine-tuning based meta-learning approaches. Li et al. [26] proposed a model-agnostic meta-learning (MAML) approach for fault diagnosis. It optimizes initial parameters to acquire prior knowledge and uses this knowledge for the fault...
我个人觉得，few-shot和meta learning不能说存在包含关系，因为他们目的不同，前者是只允许少样本，后者...
Meta-learning approaches have been proposed to tackle the few-shot learning problem.Typically, a meta-learner is trained on a variety of tasks in the hopes of being generalizable to new tasks. However, the generalizability on new tasks of a meta-learner could be fragile when it is over-train...
[Reading] Hybrid meta-learning approaches [Bayesian meta-learning] [Reading] Meta-learning for active learning, weakly-supervised learning, unsupervised learning [Lecture] Reinforcement learning primer, multi-task RL, goal-conditioned RL [Reading] Auxiliary objectives, state representation learning [Reading...
Combining Multiple Classifiers System (CMC System) for Mining Distributed Databases using Meta-Learning ApproachesData mining techniques aim to discover patterns ... EGO Badawy,A Al-Jerjawy 被引量: 0发表: 0年 Feature Selection using Distributed Ensemble Classifiers for Very Large Datasets Datasets are...
In universal prediction, current strategies often incorporate foundational principles like Occam’s Razor, which favors simpler hypotheses, and Bayesian Updating, which refines beliefs with new data. However, these traditional approaches ...