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 2776–2800 of 3569 papers

TitleStatusHype
End to End Generative Meta Curriculum Learning For Medical Data Augmentation—0
End-to-End Learning of Deep Kernel Acquisition Functions for Bayesian Optimization—0
End-to-End Offline Speech Translation System for IWSLT 2020 using Modality Agnostic Meta-Learning—0
End-to-End Simultaneous Translation System for IWSLT2020 Using Modality Agnostic Meta-Learning—0
Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent—0
Enhanced Bilevel Optimization via Bregman Distance—0
Enhancing CoMP-RSMA Performance with Movable Antennas: A Meta-Learning Optimization Framework—0
Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models—0
Enhancing Generalization of First-Order Meta-Learning—0
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
Enhancing Multi-Agent Systems via Reinforcement Learning with LLM-based Planner and Graph-based Policy—0
ERMAS: Learning Policies Robust to Reality Gaps in Multi-Agent Simulations—0
ES-Based Jacobian Enables Faster Bilevel Optimization—0
Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning—0
Evaluated CMI Bounds for Meta Learning: Tightness and Expressiveness—0
Evaluating Data Influence in Meta Learning—0
Evaluating Deep Neural Network Ensembles by Majority Voting cum Meta-Learning scheme—0
Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion—0
Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation—0
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks—0
EvoFA: Evolvable Fast Adaptation for EEG Emotion Recognition—0
Evolution of Efficient Symbolic Communication Codes—0
Evolving Domain Generalization—0
Evolving Machine Learning: A Survey—0
Evolving parametrized Loss for Image Classification Learning on Small Datasets—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