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 2651–2675 of 3569 papers

TitleStatusHype
Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL—0
Deep Reinforcement Learning, a textbook—0
Deep Reinforcement Learning for Traveling Purchaser Problems—0
Reinforcement Learning in Practice: Opportunities and Challenges—0
Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online—0
Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition—0
Deep Unrolled Meta-Learning for Multi-Coil and Multi-Modality MRI with Adaptive Optimization—0
Defending against Poisoning Backdoor Attacks on Federated Meta-learning—0
Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization—0
Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learning—0
DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization—0
Differentiable Bandit Exploration—0
Meta-Learning Bandit Policies by Gradient Ascent—0
Differentiable Meta-learning Model for Few-shot Semantic Segmentation—0
Differentiable Meta-Learning of Bandit Policies—0
Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates—0
Differentially Private Meta-Learning—0
Difficulty-aware Meta-learning for Rare Disease Diagnosis—0
Dif-MAML: Decentralized Multi-Agent Meta-Learning—0
Digital Twin-Empowered Network Planning for Multi-Tier Computing—0
DINOv2-powered Few-Shot Semantic Segmentation: A Unified Framework via Cross-Model Distillation and 4D Correlation Mining—0
Directed Variational Cross-encoder Network for Few-shot Multi-image Co-segmentation—0
Directional Domain Generalization—0
Discovering Physics Laws of Dynamical Systems via Invariant Function Learning—0
Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization—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