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 1701–1750 of 3569 papers

TitleStatusHype
AutoMLBench: A Comprehensive Experimental Evaluation of Automated Machine Learning Frameworks—0
A Meta-Learning Algorithm for Interrogative Agendas—0
Learning to Augment via Implicit Differentiation for Domain Generalization—0
Learning to Adapt via Latent Domains for Adaptive Semantic Segmentation—0
Learning to Adapt to Semantic Shift—0
Learning to Optimise General TSP Instances—0
Learning to Adapt to Online Streams with Distribution Shifts—0
Learning to Optimize on SPD Manifolds—0
Learning to Profile: User Meta-Profile Network for Few-Shot Learning—0
DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning—0
Learning to Adapt to Low-Resource Paraphrase Generation—0
DMSD-CDFSAR: Distillation from Mixed-Source Domain for Cross-Domain Few-shot Action Recognition—0
Auto-Meta: Automated Gradient Based Meta Learner Search—0
A Meta-learner for Heterogeneous Effects in Difference-in-Differences—0
Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection—0
Learning to Recover from Failures using Memory—0
Learning to Adapt Multi-View Stereo by Self-Supervision—0
DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking and Loop-Closing—0
Learning to Actively Learn: A Robust Approach—0
Learning Tensor Representations for Meta-Learning—0
Diverse Inference and Verification for Advanced Reasoning—0
Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation—0
Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Data—0
Learning to Sample: an Active Learning Framework—0
Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs—0
Learning State-Dependent Losses for Inverse Dynamics Learning—0
Learning Soft Labels via Meta Learning—0
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP—0
Automatic Unsupervised Outlier Model Selection—0
Learning to Select Best Forecast Tasks for Clinical Outcome Prediction—0
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation—0
Learning Prototype-oriented Set Representations for Meta-Learning—0
Instance-Conditional Timescales of Decay for Non-Stationary Learning—0
Learning to Switch CNNs with Model Agnostic Meta Learning for Fine Precision Visual Servoing—0
Learning to Tune XGBoost with XGBoost—0
Learning to Unlearn for Robust Machine Unlearning—0
Learning to Update for Object Tracking with Recurrent Meta-learner—0
Divergent Search for Few-Shot Image Classification—0
Learning Online for Unified Segmentation and Tracking Models—0
Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks—0
Learning Unsupervised Learning Rules—0
Learning via Surrogate PAC-Bayes—0
Automatic tuning of hyper-parameters of reinforcement learning algorithms using Bayesian optimization with behavioral cloning—0
A Meta-heuristic Approach to Estimate and Explain Classifier Uncertainty—0
A Meta-GNN approach to personalized seizure detection and classification—0
A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics—0
Learning Not to Learn: Nature versus Nurture in Silico—0
Distribution Embedding Network for Meta-Learning with Variable-Length Input—0
Learning Neural Processes on the Fly—0
Distributionally robust minimization in meta-learning for system identification—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