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 901–925 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
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP—0
Betty: An Automatic Differentiation Library for Multilevel Optimization—0
Efficient Unified Demosaicing for Bayer and Non-Bayer Patterned Image Sensors—0
Elastically-Constrained Meta-Learner for Federated Learning—0
A Meta-GNN approach to personalized seizure detection and classification—0
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning—0
Electrocardiogram-Language Model for Few-Shot Question Answering with Meta Learning—0
ELF-UA: Efficient Label-Free User Adaptation in Gaze Estimation—0
Divergent Search for Few-Shot Image Classification—0
Eliminating Meta Optimization Through Self-Referential Meta Learning—0
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning—0
Embracing assay heterogeneity with neural processes for markedly improved bioactivity predictions—0
Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks—0
Automatic tuning of hyper-parameters of reinforcement learning algorithms using Bayesian optimization with behavioral cloning—0
EMO: Episodic Memory Optimization for Few-Shot Meta-Learning—0
Emotional RobBERT and Insensitive BERTje: Combining Transformers and Affect Lexica for Dutch Emotion Detection—0
A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics—0
EMPL: A novel Efficient Meta Prompt Learning Framework for Few-shot Unsupervised Domain Adaptation—0
Few-Shot Histopathology Image Classification: Evaluating State-of-the-Art Methods and Unveiling Performance Insights—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