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 3151–3200 of 3569 papers

TitleStatusHype
Learning Efficient and Effective Exploration Policies with Counterfactual Meta Policy—0
Learning Fast Sample Re-weighting Without Reward Data—0
Learning Feature Relevance Through Step Size Adaptation in Temporal-Difference Learning—0
Reconstruction guided Meta-learning for Few Shot Open Set Recognition—0
Learning from Few Examples: A Summary of Approaches to Few-Shot Learning—0
Learning from Few Samples: A Survey—0
Learning from My Friends: Few-Shot Personalized Conversation Systems via Social Networks—0
Learning from Noisy Demonstration Sets via Meta-Learned Suitability Assessor—0
Learning from Noisy Labels via Self-Taught On-the-Fly Meta Loss Rescaling—0
Learning from the Past: Continual Meta-Learning via Bayesian Graph Modeling—0
A Survey on Machine Learning from Few Samples—0
Learning Functional Priors and Posteriors from Data and Physics—0
Learning Generative Prior with Latent Space Sparsity Constraints—0
Learning Intrinsic and Extrinsic Intentions for Cold-start Recommendation with Neural Stochastic Processes—0
Learning Knowledge Representation with Meta Knowledge Distillation for Single Image Super-Resolution—0
Learning Low-dimensional Latent Dynamics from High-dimensional Observations: Non-asymptotics and Lower Bounds—0
Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment—0
Learning Modality Knowledge Alignment for Cross-Modality Transfer—0
Learning Neural Processes on the Fly—0
Learning Not to Learn: Nature versus Nurture in Silico—0
Learning Online for Unified Segmentation and Tracking Models—0
Instance-Conditional Timescales of Decay for Non-Stationary Learning—0
Learning Prototype-oriented Set Representations for Meta-Learning—0
Learning Soft Labels via Meta Learning—0
Learning State-Dependent Losses for Inverse Dynamics Learning—0
Learning Tensor Representations for Meta-Learning—0
Learning to Actively Learn: A Robust Approach—0
Learning to Adapt Multi-View Stereo by Self-Supervision—0
Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection—0
Learning to Adapt to Low-Resource Paraphrase Generation—0
Learning to Adapt to Online Streams with Distribution Shifts—0
Learning to Adapt to Semantic Shift—0
Learning to Adapt via Latent Domains for Adaptive Semantic Segmentation—0
Learning to Augment via Implicit Differentiation for Domain Generalization—0
Learning to Bound the Multi-class Bayes Error—0
Learning to Classify Intents and Slot Labels Given a Handful of Examples—0
Is Nash Equilibrium Approximator Learnable?—0
Learning to Cope with Adversarial Attacks—0
Learning to Focus: Cascaded Feature Matching Network for Few-shot Image Recognition—0
Learning to Generalize to Unseen Tasks with Bilevel Optimization—0
Learning to Generalize Unseen Domains via Multi-Source Meta Learning for Text Classification—0
Learning to Generalize without Bias for Open-Vocabulary Action Recognition—0
Learning to generate imaginary tasks for improving generalization in meta-learning—0
Learning to Identify Physical Laws of Hamiltonian Systems via Meta-Learning—0
Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects—0
Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?—0
Learning to Learn a Cold-start Sequential Recommender—0
Learning to Learn across Diverse Data Biases in Deep Face Recognition—0
Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification—0
Learning To Learn Around A Common Mean—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