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 2051–2100 of 3569 papers

TitleStatusHype
Towards a population-informed approach to the definition of data-driven models for structural dynamics—0
Towards Automated Error Analysis: Learning to Characterize Errors—0
Towards Better Meta-Initialization with Task Augmentation for Kindergarten-aged Speech Recognition—0
Towards explainable meta-learning—0
Reconsidering Learning Objectives in Unbiased Recommendation with Unobserved Confounders—0
Towards Discriminative Representation with Meta-learning for Colonoscopic Polyp Re-Identification—0
Towards Efficient and Effective Alignment of Large Language Models—0
Towards Few-Annotation Learning in Computer Vision: Application to Image Classification and Object Detection tasks—0
Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts—0
Towards General and Efficient Online Tuning for Spark—0
Towards Generalizable Personalized Federated Learning with Adaptive Local Adaptation—0
Towards Generalization on Real Domain for Single Image Dehazing via Meta-Learning—0
Towards Intelligent Pick and Place Assembly of Individualized Products Using Reinforcement Learning—0
Towards intervention-centric causal reasoning in learning agents—0
Towards Learning to Remember in Meta Learning of Sequential Domains—0
Towards Low-Resource Semi-Supervised Dialogue Generation with Meta-Learning—0
Towards meta-learning for multi-target regression problems—0
Towards more efficient agricultural practices via transformer-based crop type classification—0
Towards Multi-Domain Single Image Dehazing via Test-Time Training—0
Towards Reliable Neural Machine Translation with Consistency-Aware Meta-Learning—0
Towards Robust and Interpretable EMG-based Hand Gesture Recognition using Deep Metric Meta Learning—0
Towards Robust Graph Neural Networks against Label Noise—0
Towards Robust Physical-world Backdoor Attacks on Lane Detection—0
Towards robust prediction of material properties for nuclear reactor design under scarce data -- a study in creep rupture property—0
Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework—0
Towards Sharper Information-theoretic Generalization Bounds for Meta-Learning—0
Towards Sleep Scoring Generalization Through Self-Supervised Meta-Learning—0
Towards Subject Agnostic Affective Emotion Recognition—0
Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search—0
Towards Understanding Generalization in Gradient-Based Meta-Learning—0
Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap for Prompt-Based Large Language Models and Beyond—0
Hyperparameter Optimization for Unsupervised Outlier Detection—0
Towards Zero-Shot Learning with Fewer Seen Class Examples—0
Tracking by Instance Detection: A Meta-Learning Approach—0
Training an Interactive Helper—0
Training Data Generating Networks: Shape Reconstruction via Bi-level Optimization—0
Training few-shot classification via the perspective of minibatch and pretraining—0
Trajectory-Based Meta-Learning for Out-Of-Vocabulary Word Embedding Learning—0
Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning—0
Transferable Sequential Recommendation via Vector Quantized Meta Learning—0
Transfer-based Adversarial Poisoning Attacks for Online (MIMO-)Deep Receviers—0
Transfering Hierarchical Structure with Dual Meta Imitation Learning—0
Transfer Learning for Algorithm Recommendation—0
Transfer Learning for CSI-based Positioning with Multi-environment Meta-learning—0
Transfer Learning for Finetuning Large Language Models—0
Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users—0
Transfer Meta-Learning: Information-Theoretic Bounds and Information Meta-Risk Minimization—0
Transferring Hierarchical Structure with Dual Meta Imitation Learning—0
Transferring SLU Models in Novel Domains—0
Transformation Invariant Few-Shot Object Detection—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