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

Showing 12011225 of 3569 papers

TitleStatusHype
Instance-Conditional Timescales of Decay for Non-Stationary Learning0
Transductive Linear Probing: A Novel Framework for Few-Shot Node ClassificationCode1
Task-Adaptive Meta-Learning Framework for Advancing Spatial GeneralizabilityCode0
General-Purpose In-Context Learning by Meta-Learning TransformersCode0
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive LearningCode1
Learning Domain Invariant Prompt for Vision-Language ModelsCode1
Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning0
Few-Shot Preference Learning for Human-in-the-Loop RL0
Meta-Learning Fast Weight Language Models0
Domain-General Crowd Counting in Unseen ScenariosCode1
DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization0
Meta Learning for Few-Shot Medical Text Classification0
Meta-Shop: Improving Item Advertisement For Small Businesses0
Adaptive Robust Model Predictive Control via Uncertainty Cancellation0
Towards Cross Domain Generalization of Hamiltonian Representation via Meta Learning0
On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learning0
Generalized Face Anti-Spoofing via Multi-Task Learning and One-Side Meta Triplet Loss0
Rethinking the Number of Shots in Robust Model-Agnostic Meta-Learning0
Breaking Immutable: Information-Coupled Prototype Elaboration for Few-Shot Object Detection0
Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation ModelsCode1
Multi-Modal Few-Shot Temporal Action DetectionCode1
Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task LearningCode0
Meta-Learning for Automated Selection of Anomaly Detectors for Semi-Supervised Datasets0
Meta-Learning the Inductive Biases of Simple Neural CircuitsCode0
Learning to Rasterize DifferentiablyCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified