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 with no code
DSDRNet: Disentangling Representation and Reconstruct Network for Domain Generalization
Domain generalization faces challenges due to the distribution shift between training and testing sets, and the presence of unseen target domains.
Data-Driven Performance Guarantees for Classical and Learned Optimizers
We build generalization guarantees for classical optimizers, using a sample convergence bound, and for learned optimizers, using the Probably Approximately Correct (PAC)-Bayes framework.
MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models
The framework consists of two phases: the (offine) meta-training phase learns a aggregated NSSM using data from source systems, and the (online) meta-inference phase quickly adapts this aggregated model to the target system using only a few data points and few online training iterations, based on local loss function gradients.
Few-shot Name Entity Recognition on StackOverflow
StackOverflow, with its vast question repository and limited labeled examples, raise an annotation challenge for us.
Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Model
The results show that our meta-trained model performs significantly better than standard fine-tuning methods, outperforming the baseline fine-tuning in macro F1 score with 18\% and 0. 8\% over XLM-R and mBERT.
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.
