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

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Showing 901–910 of 3569 papers

TitleStatusHype
Efficient meta reinforcement learning via meta goal generation—0
MGHRL: Meta Goal-generation for Hierarchical Reinforcement Learning—0
Efficient Model Compression Techniques with FishLeg—0
Efficient Model Selection for Time Series Forecasting via LLMs—0
Efficient Neural Representation of Volumetric Data using Coordinate-Based Networks—0
BERT Learns to Teach: Knowledge Distillation with Meta Learning—0
Efficient Task Grouping Through Samplewise Optimisation Landscape Analysis—0
Betty: An Automatic Differentiation Library for Multilevel Optimization—0
Efficient Unified Demosaicing for Bayer and Non-Bayer Patterned Image Sensors—0
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP—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