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 626–650 of 3569 papers

TitleStatusHype
Single Neuromorphic Memristor closely Emulates Multiple Synaptic Mechanisms for Energy Efficient Neural Networks—0
Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation—0
Informed Meta-Learning—0
Structural Knowledge-Driven Meta-Learning for Task Offloading in Vehicular Networks with Integrated Communications, Sensing and Computing—0
LiMAML: Personalization of Deep Recommender Models via Meta Learning—0
A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends—0
Generalizing Reward Modeling for Out-of-Distribution Preference LearningCode0
Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap for Prompt-Based Large Language Models and Beyond—0
Referee-Meta-Learning for Fast Adaptation of Locational Fairness—0
On Sensitivity of Learning with Limited Labelled Data to the Effects of Randomness: Impact of Interactions and Systematic ChoicesCode0
Function Class Learning with Genetic Programming: Towards Explainable Meta Learning for Tumor Growth Functionals—0
Bridging or Breaking: Impact of Intergroup Interactions on Religious Polarization—0
Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness—0
One-shot Imitation in a Non-Stationary Environment via Multi-Modal Skill—0
MetaTra: Meta-Learning for Generalized Trajectory Prediction in Unseen Domain—0
Interactive singing melody extraction based on active adaptation—0
Distilling Symbolic Priors for Concept Learning into Neural Networks—0
Discovering Temporally-Aware Reinforcement Learning AlgorithmsCode1
Learning mirror maps in policy mirror descent—0
Progressive Conservative Adaptation for Evolving Target Domains—0
Meet JEANIE: a Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignment—0
More Flexible PAC-Bayesian Meta-Learning by Learning Learning AlgorithmsCode0
Learning a Decision Tree Algorithm with TransformersCode2
Is Mamba Capable of In-Context Learning?Code1
Automatic Combination of Sample Selection Strategies for Few-Shot Learning—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