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 976–1000 of 3569 papers

TitleStatusHype
Boosting Meta-Training with Base Class Information for Few-Shot Learning—0
Boosting Model Resilience via Implicit Adversarial Data Augmentation—0
Task-Robust Model-Agnostic Meta-Learning—0
Distributed Representations of Words and Documents for Discriminating Similar Languages—0
A meta-algorithm for classification using random recursive tree ensembles: A high energy physics application—0
Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology—0
Boosting Natural Language Generation from Instructions with Meta-Learning—0
A Communication and Computation Efficient Fully First-order Method for Decentralized Bilevel Optimization—0
Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling—0
Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks—0
Amortized Proximal Optimization—0
Parallel Momentum Methods Under Biased Gradient Estimations—0
Automatic low-bit hybrid quantization of neural networks through meta learning—0
Brain-inspired global-local learning incorporated with neuromorphic computing—0
Amsqr at SemEval-2022 Task 4: Towards AutoNLP via Meta-Learning and Adversarial Data Augmentation for PCL Detection—0
Distributed Evolution Strategies Using TPUs for Meta-Learning—0
Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study—0
Distributed Estimation by Two Agents with Different Feature Spaces—0
Automatic Learning to Detect Concept Drift—0
Exploring the Efficacy of Meta-Learning: Unveiling Superior Data Diversity Utilization of MAML Over Pre-training—0
Bridging or Breaking: Impact of Intergroup Interactions on Religious Polarization—0
Amazon SageMaker Autopilot: a white box AutoML solution at scale—0
A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation—0
DistPro: Searching A Fast Knowledge Distillation Process via Meta Optimization—0
Distilling Symbolic Priors for Concept Learning into Neural Networks—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