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 1351–1400 of 3569 papers

TitleStatusHype
Knowledge-driven Meta-learning for CSI Feedback—0
Bayesian Active Learning in the Presence of Nuisance Parameters—0
Meta learning with language models: Challenges and opportunities in the classification of imbalanced textCode0
Are LSTMs Good Few-Shot Learners?Code0
Towards Subject Agnostic Affective Emotion Recognition—0
Meta-learning of Physics-informed Neural Networks for Efficiently Solving Newly Given PDEs—0
Solving Expensive Optimization Problems in Dynamic Environments with Meta-learningCode0
Are Structural Concepts Universal in Transformer Language Models? Towards Interpretable Cross-Lingual GeneralizationCode0
Unsupervised Representation Learning to Aid Semi-Supervised Meta LearningCode0
Bayesian Meta-Learning for Improving Generalizability of Health Prediction Models With Similar Causal MechanismsCode0
BioAct-Het: A Heterogeneous Siamese Neural Network for Bioactivity Prediction Using Novel Bioactivity RepresentatioCode0
Dynamic Link Prediction for New Nodes in Temporal Graph Networks—0
A Partially Supervised Reinforcement Learning Framework for Visual Active SearchCode0
Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks—0
In-Context Learning for Few-Shot Molecular Property Prediction—0
Subspace Adaptation Prior for Few-Shot LearningCode0
Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning—0
Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding—0
Reset It and Forget It: Relearning Last-Layer Weights Improves Continual and Transfer Learning—0
Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters—0
Federated Multi-Level Optimization over Decentralized Networks—0
Understanding Transfer Learning and Gradient-Based Meta-Learning TechniquesCode0
A Meta-Learning Perspective on Transformers for Causal Language Modeling—0
Early Warning Prediction with Automatic Labeling in Epilepsy Patients—0
Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspective—0
Task Aware Modulation using Representation Learning: An Approach for Few Shot Learning in Environmental Systems—0
RTDK-BO: High Dimensional Bayesian Optimization with Reinforced Transformer Deep kernels—0
SHOT: Suppressing the Hessian along the Optimization Trajectory for Gradient-Based Meta-LearningCode0
Federated Conditional Stochastic Optimization—0
PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property PredictionCode0
A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm—0
On the Role of Neural Collapse in Meta Learning Models for Few-shot LearningCode0
It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density EstimationCode0
Generative Semi-supervised Learning with Meta-Optimized Synthetic Samples—0
Cold & Warm Net: Addressing Cold-Start Users in Recommender Systems—0
SAVME: Efficient Safety Validation for Autonomous Systems Using Meta-Learning—0
Semi-Supervised Variational Inference over Nonlinear Channels—0
AutoSynth: Learning to Generate 3D Training Data for Object Point Cloud Registration—0
A Systematic Review of Few-Shot Learning in Medical Imaging—0
Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI—0
MAD: Meta Adversarial Defense BenchmarkCode0
MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification—0
Generalizable Neural Fields as Partially Observed Neural Processes—0
BatMan-CLR: Making Few-shots Meta-Learners Resilient Against Label Noise—0
Convergence of Gradient-based MAML in LQR—0
A supervised generative optimization approach for tabular data—0
Retrieval-Augmented Meta Learning for Low-Resource Text Classification—0
Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays—0
Amortised Inference in Bayesian Neural NetworksCode0
Towards General and Efficient Online Tuning for Spark—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