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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 676–700 of 3569 papers

TitleStatusHype
A Universal Knowledge Model and Cognitive Architecture for Prototyping AGI—0
Secrets of RLHF in Large Language Models Part II: Reward ModelingCode5
Any-Way Meta Learning—0
G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender Systems—0
Meta-forests: Domain generalization on random forests with meta-learning—0
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs—0
Marginal Debiased Network for Fair Visual Recognition—0
MoML: Online Meta Adaptation for 3D Human Motion Prediction—0
Fast Adaptation for Human Pose Estimation via Meta-Optimization—0
Positive-Unlabeled Learning by Latent Group-Aware Meta DisambiguationCode1
Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real WorldCode1
Meta Reinforcement Learning for Multi-Task Offloading in Vehicular Edge Computing—0
Selective-Memory Meta-Learning with Environment Representations for Sound Event Localization and DetectionCode1
Exploring intra-task relations to improve meta-learning algorithms—0
Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype EnhancementCode1
Hybrid-Task Meta-Learning: A Graph Neural Network Approach for Scalable and Transferable Bandwidth Allocation—0
Meta-Learning-Based Adaptive Stability Certificates for Dynamical SystemsCode0
Personalized Federated Learning with Contextual Modulation and Meta-LearningCode0
Discovering modular solutions that generalize compositionallyCode1
Meta Transfer of Self-Supervised Knowledge: Foundation Model in Action for Post-Traumatic Epilepsy Prediction—0
Meta-Learning with Versatile Loss Geometries for Fast Adaptation Using Mirror DescentCode0
AutoXPCR: Automated Multi-Objective Model Selection for Time Series ForecastingCode0
Scaling Opponent Shaping to High Dimensional Games—0
POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning—0
Outlier detection using flexible categorisation and interrogative agendas—0
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All datasetsML10MT50ML45OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8—Unverified
2MZMeta-train success rate97.6—Unverified
3MAMLMeta-test success rate36—Unverified
4RL^2Meta-test success rate10—Unverified
5DnCMeta-test success rate5.4—Unverified
6PEARLMeta-test success rate0—Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60—Unverified
2Multi-task multi-head SACAverage Success Rate35.85—Unverified
3DisCorAverage Success Rate26—Unverified
4NDPAverage Success Rate11—Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5—Unverified
2MZMeta-test success rate (zero-shot)17.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75—Unverified