Meta-Learning

1169 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 implementations

Predicting Configuration Performance in Multiple Environments with Sequential Meta-learning

ideas-labo/sempl 5 Feb 2024

Through comparing with 15 state-of-the-art models under nine systems, our extensive experimental results demonstrate that SeMPL performs considerably better on 89% of the systems with up to 99% accuracy improvement, while being data-efficient, leading to a maximum of 3. 86x speedup.

0
05 Feb 2024

Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning

gmc-drl/symbol 4 Feb 2024

Recent Meta-learning for Black-Box Optimization (MetaBBO) methods harness neural networks to meta-learn configurations of traditional black-box optimizers.

6
04 Feb 2024

Sample Weight Estimation Using Meta-Updates for Online Continual Learning

hamedhemati/omsi 29 Jan 2024

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.

0
29 Jan 2024

Learning Universal Predictors

google-deepmind/neural_networks_solomonoff_induction 26 Jan 2024

Meta-learning has emerged as a powerful approach to train neural networks to learn new tasks quickly from limited data.

43
26 Jan 2024

Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML Approach for the Model-free LQR

jd-anderson/maml-lqr 25 Jan 2024

We investigate the problem of learning Linear Quadratic Regulators (LQR) in a multi-task, heterogeneous, and model-free setting.

0
25 Jan 2024

A Cost-Sensitive Meta-Learning Strategy for Fair Provider Exposure in Recommendation

alessandraperniciano/meta-learning-strategy-fair-provider-exposure 24 Jan 2024

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.

3
24 Jan 2024

Fine-Grained Prototypes Distillation for Few-Shot Object Detection

wangchen1801/fpd 15 Jan 2024

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.

12
15 Jan 2024

Window Stacking Meta-Models for Clinical EEG Classification

zhuyixuan1997/eegscopeandarbitration 14 Jan 2024

Windowing is a common technique in EEG machine learning classification and other time series tasks.

1
14 Jan 2024

Secrets of RLHF in Large Language Models Part II: Reward Modeling

openlmlab/moss-rlhf 11 Jan 2024

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.

1,141
11 Jan 2024

Selective-Memory Meta-Learning with Environment Representations for Sound Event Localization and Detection

jinbo-hu/seld-data-generator 27 Dec 2023

In addition, we introduce environment representations to characterize different acoustic settings, enhancing the adaptability of our attenuation approach to various environments.

1
27 Dec 2023