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
Use these libraries to find Meta-Learning models and implementationsDatasets
TyDiQA
Meta-Dataset
M4
Omniglot
BlendedMVS
TyDiQA-GoldP
Kuzushiji-49
Latest papers with no code
Multi-task Magnetic Resonance Imaging Reconstruction using Meta-learning
Using single-task deep learning methods to reconstruct Magnetic Resonance Imaging (MRI) data acquired with different imaging sequences is inherently challenging.
MetaCap: Meta-learning Priors from Multi-View Imagery for Sparse-view Human Performance Capture and Rendering
Our key idea is to meta-learn the radiance field weights solely from potentially sparse multi-view videos, which can serve as a prior when fine-tuning them on sparse imagery depicting the human.
Few-Shot Cross-System Anomaly Trace Classification for Microservice-based systems
Within the same MSS, our framework achieves an average accuracy of 93. 26\% and 85. 2\% across 50 meta-testing tasks for Trainticket and OnlineBoutique, respectively, when provided with 10 instances for each task.
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification
Background and objective: Employing deep learning models in critical domains such as medical imaging poses challenges associated with the limited availability of training data.
A Moreau Envelope Approach for LQR Meta-Policy Estimation
We study the problem of policy estimation for the Linear Quadratic Regulator (LQR) in discrete-time linear time-invariant uncertain dynamical systems.
Advancing Extrapolative Predictions of Material Properties through Learning to Learn
Recent advancements in machine learning have showcased its potential to significantly accelerate the discovery of new materials.
CoLLEGe: Concept Embedding Generation for Large Language Models
Current language models are unable to quickly learn new concepts on the fly, often requiring a more involved finetuning process to learn robustly.
Meta-learning of data-driven controllers with automatic model reference tuning: theory and experimental case study
Data-driven control offers a viable option for control scenarios where constructing a system model is expensive or time-consuming.
Learning-to-Learn the Wave Angle Estimation
A precise incident wave angle estimation in aerial communication is a key enabler in sixth-generation wireless communication network.
Network bottlenecks and task structure control the evolution of interpretable learning rules in a foraging agent
Developing reliable mechanisms for continuous local learning is a central challenge faced by biological and artificial systems.
