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 1951–1975 of 3569 papers

TitleStatusHype
Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization—0
Set Based Stochastic Subsampling—0
Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling—0
Stress Testing of Meta-learning Approaches for Few-shot Learning—0
Structural Knowledge-Driven Meta-Learning for Task Offloading in Vehicular Networks with Integrated Communications, Sensing and Computing—0
Style-Guided Domain Adaptation for Face Presentation Attack Detection—0
StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval—0
Subgraph-aware Few-Shot Inductive Link Prediction via Meta-Learning—0
Successive Model-Agnostic Meta-Learning for Few-Shot Fault Time Series Prognosis—0
Towards Explainable Meta-Learning for DDoS Detection—0
SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search—0
SUM: Suboptimal Unitary Multi-task Learning Framework for Spatiotemporal Data Prediction—0
SuperCone: Unified User Segmentation over Heterogeneous Experts via Concept Meta-learning—0
Supplementary Meta-Learning: Towards a Dynamic Model for Deep Neural Networks—0
Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation—0
SuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification—0
Symbiotic Message Passing Model for Transfer Learning between Anti-Fungal and Anti-Bacterial Domains—0
Synthetic speech detection using meta-learning with prototypical loss—0
System 2 Reasoning via Generality and Adaptation—0
System-Agnostic Meta-Learning for MDP-based Dynamic Scheduling via Descriptive Policy—0
System Identification via Meta-Learning in Linear Time-Varying Environments—0
Tailored Conversations beyond LLMs: A RL-Based Dialogue Manager—0
Tailored Forecasting from Short Time Series via Meta-learning—0
Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time—0
Taming the Herd: Multi-Modal Meta-Learning with a Population of Agents—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