SOTAVerified

Meta-Learning

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 )

Papers

Showing 33013325 of 3569 papers

TitleStatusHype
A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-LearningCode0
Black box meta-learning intrinsic rewards for sparse-reward environmentsCode0
Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement LearningCode0
Adaptation-Agnostic Meta-TrainingCode0
Online Parameter-Free Learning of Multiple Low Variance TasksCode0
Deep Multimodal Fusion for Generalizable Person Re-identificationCode0
Unsupervised Representation Learning to Aid Semi-Supervised Meta LearningCode0
Guided evolutionary strategies: Augmenting random search with surrogate gradientsCode0
Online-Within-Online Meta-LearningCode0
BioAct-Het: A Heterogeneous Siamese Neural Network for Bioactivity Prediction Using Novel Bioactivity RepresentatioCode0
Meta-learning from Tasks with Heterogeneous Attribute SpacesCode0
Unsupervised Task Clustering for Multi-Task Reinforcement LearningCode0
Deep Learning Theory Review: An Optimal Control and Dynamical Systems PerspectiveCode0
A MIND for Reasoning: Meta-learning for In-context DeductionCode0
Task Augmentation by Rotating for Meta-LearningCode0
Meta-learning how to Share Credit among Macro-ActionsCode0
Deep Compressed SensingCode0
SeFNet: Bridging Tabular Datasets with Semantic Feature NetsCode0
Benchmarking and Improving Compositional Generalization of Multi-aspect Controllable Text GenerationCode0
Decomposed Meta-Learning for Few-Shot Sequence LabelingCode0
On Provably Robust Meta-Bayesian OptimizationCode0
On Sensitivity of Learning with Limited Labelled Data to the Effects of Randomness: Impact of Interactions and Systematic ChoicesCode0
Decoder Choice Network for Meta-LearningCode0
Meta-Learning Initializations for Image SegmentationCode0
Are LSTMs Good Few-Shot Learners?Code0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified