Meta-Learning
1187 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
Hybrid Multi-stage Decoding for Few-shot NER with Entity-aware Contrastive Learning
In the training process, we train and get the best entity-span detection model and the entity classification model separately on the source domain using meta-learning, where we create a contrastive learning module to enhance entity representations for entity classification.
Optimization of Lightweight Malware Detection Models For AIoT Devices
Malware intrusion is problematic for Internet of Things (IoT) and Artificial Intelligence of Things (AIoT) devices as they often reside in an ecosystem of connected devices, such as a smart home.
Vision Transformers in Domain Adaptation and Generalization: A Study of Robustness
Motivated by the increased interest from the research community, our paper investigates the deployment of vision transformers in domain adaptation and domain generalization scenarios.
Domain Generalization through Meta-Learning: A Survey
Deep neural networks (DNNs) have revolutionized artificial intelligence but often lack performance when faced with out-of-distribution (OOD) data, a common scenario due to the inevitable domain shifts in real-world applications.
Deep Reinforcement Learning for Traveling Purchaser Problems
The traveling purchaser problem (TPP) is an important combinatorial optimization problem with broad applications.
Is Meta-training Really Necessary for Molecular Few-Shot Learning ?
Few-shot learning has recently attracted significant interest in drug discovery, with a recent, fast-growing literature mostly involving convoluted meta-learning strategies.
Foundations of Cyber Resilience: The Confluence of Game, Control, and Learning Theories
This chapter starts with a systemic view toward cyber risks and presents the confluence of game theory, control theory, and learning theories, which are three major pillars for the design of cyber resilience mechanisms to counteract increasingly sophisticated and evolving threats in our networks and organizations.
Meta Learning in Bandits within Shared Affine Subspaces
We study the problem of meta-learning several contextual stochastic bandits tasks by leveraging their concentration around a low-dimensional affine subspace, which we learn via online principal component analysis to reduce the expected regret over the encountered bandits.
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.
