Meta-Learning
1183 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
Use these libraries to find Meta-Learning models and implementationsDatasets
TyDiQA
Meta-Dataset
M4
Omniglot
BlendedMVS
TyDiQA-GoldP
Kuzushiji-49
Latest papers
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
wangchen1801/fpd
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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
zhuyixuan1997/eegscopeandarbitration
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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.
Personalized Federated Learning with Contextual Modulation and Meta-Learning
These findings highlight the potential of incorporating contextual information and meta-learning techniques into federated learning, paving the way for advancements in distributed machine learning paradigms.
Meta-Learning-Based Adaptive Stability Certificates for Dynamical Systems
This paper addresses the problem of Neural Network (NN) based adaptive stability certification in a dynamical system.
Discovering modular solutions that generalize compositionally
smonsays/modular-hyperteacher
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This allows us to relate the problem of compositional generalization to that of identification of the underlying modules.
