Meta-Learning
1189 papers with code • 4 benchmarks • 19 datasets
Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.
( Image credit: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks )
Libraries
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Latest papers
Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning
Recent Meta-learning for Black-Box Optimization (MetaBBO) methods harness neural networks to meta-learn configurations of traditional black-box optimizers.
Sample Weight Estimation Using Meta-Updates for Online Continual Learning
This is done by first estimating sample weight parameters for each sample in the mini-batch, then, updating the model with the adapted sample weights.
Learning Universal Predictors
Meta-learning has emerged as a powerful approach to train neural networks to learn new tasks quickly from limited data.
Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML Approach for the Model-free LQR
We investigate the problem of learning Linear Quadratic Regulators (LQR) in a multi-task, heterogeneous, and model-free setting.
A Cost-Sensitive Meta-Learning Strategy for Fair Provider Exposure in Recommendation
When devising recommendation services, it is important to account for the interests of all content providers, encompassing not only newcomers but also minority demographic groups.
Fine-Grained Prototypes Distillation for Few-Shot Object Detection
However, the class-level prototypes are difficult to precisely generate, and they also lack detailed information, leading to instability in performance. New methods are required to capture the distinctive local context for more robust novel object detection.
Window Stacking Meta-Models for Clinical EEG Classification
Windowing is a common technique in EEG machine learning classification and other time series tasks.
Secrets of RLHF in Large Language Models Part II: Reward Modeling
We introduce a series of novel methods to mitigate the influence of incorrect and ambiguous preferences in the dataset and fully leverage high-quality preference data.
Selective-Memory Meta-Learning with Environment Representations for Sound Event Localization and Detection
In addition, we introduce environment representations to characterize different acoustic settings, enhancing the adaptability of our attenuation approach to various environments.
Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype Enhancement
In this paper, we propose a novel framework based on the adapter mechanism, namely Adaptive FSS, which can efficiently adapt the existing FSS model to the novel classes.