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 1676–1700 of 3569 papers

TitleStatusHype
Asynchronous Distributed Bilevel OptimizationCode0
AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning—0
Cognitive Level-k Meta-Learning for Safe and Pedestrian-Aware Autonomous Driving—0
One-shot skill assessment in high-stakes domains with limited data via meta learningCode0
Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs—0
Selective classification using a robust meta-learning approach—0
Instance-Conditional Timescales of Decay for Non-Stationary Learning—0
Task-Adaptive Meta-Learning Framework for Advancing Spatial GeneralizabilityCode0
General-Purpose In-Context Learning by Meta-Learning TransformersCode0
Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning—0
Few-Shot Preference Learning for Human-in-the-Loop RL—0
Meta-Learning Fast Weight Language Models—0
DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization—0
Meta Learning for Few-Shot Medical Text Classification—0
Adaptive Robust Model Predictive Control via Uncertainty Cancellation—0
Towards Cross Domain Generalization of Hamiltonian Representation via Meta Learning—0
On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learning—0
Meta-Shop: Improving Item Advertisement For Small Businesses—0
Generalized Face Anti-Spoofing via Multi-Task Learning and One-Side Meta Triplet Loss—0
Rethinking the Number of Shots in Robust Model-Agnostic Meta-Learning—0
Breaking Immutable: Information-Coupled Prototype Elaboration for Few-Shot Object Detection—0
Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task LearningCode0
Meta-Learning for Automated Selection of Anomaly Detectors for Semi-Supervised Datasets—0
Meta-Learning the Inductive Biases of Simple Neural CircuitsCode0
Learning to Rasterize DifferentiablyCode0
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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