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 2751–2800 of 3569 papers

TitleStatusHype
Meta-Learning of NAS for Few-shot Learning in Medical Image Applications—0
Meta-Learning of Neural State-Space Models Using Data From Similar Systems—0
Meta-learning of Physics-informed Neural Networks for Efficiently Solving Newly Given PDEs—0
Meta-learning of semi-supervised learning from tasks with heterogeneous attribute spaces—0
Meta-learning of Sequential Strategies—0
Meta-learning of shared linear representations beyond well-specified linear regression—0
Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection—0
Meta-Learning Online Control for Linear Dynamical Systems—0
Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving—0
Meta-learning on Spectral Images of Electroencephalogram of Schizophenics—0
Sample-Efficient Linear Representation Learning from Non-IID Non-Isotropic Data—0
Meta-Learning over Time for Destination Prediction Tasks—0
Meta-Learning PAC-Bayes Priors in Model Averaging—0
Meta-Learning Parameterized First-Order Optimizers using Differentiable Convex Optimization—0
Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks—0
Meta-learning PINN loss functions—0
Meta-Learning Priors for Safe Bayesian Optimization—0
Meta-learning Pseudo-differential Operators with Deep Neural Networks—0
Meta-Learning Regrasping Strategies for Physical-Agnostic Objects—0
Meta-Learning Reliable Priors in the Function Space—0
Meta-learning representations for clustering with infinite Gaussian mixture models—0
Meta-learning Representations for Learning from Multiple Annotators—0
Meta-Learning Requires Meta-Augmentation—0
Meta-learning richer priors for VAEs—0
Meta-Learning Runge-Kutta—0
Meta-learning: searching in the model space—0
Meta-learning Slice-to-Volume Reconstruction in Fetal Brain MRI using Implicit Neural Representations—0
Meta-Learning Sparse Compression Networks—0
Meta-Learning Strategies through Value Maximization in Neural Networks—0
Meta-Learning surrogate models for sequential decision making—0
Meta Learning Text-to-Speech Synthesis in over 7000 Languages—0
Meta-learning the Learning Trends Shared Across Tasks—0
Learning mirror maps in policy mirror descent—0
Meta Learning the Step Size in Policy Gradient Methods—0
Meta-learning to Calibrate Gaussian Processes with Deep Kernels for Regression Uncertainty Estimation—0
Meta learning to classify intent and slot labels with noisy few shot examples—0
Meta-Learning to Cluster—0
Meta-Learning to Detect Rare Objects—0
Meta-Learning to Explore via Memory Density Feedback—0
Meta-Learning to Guide Segmentation—0
Meta-Learning to Improve Pre-Training—0
Meta Learning to Rank for Sparsely Supervised Queries—0
Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning—0
Meta-learning Transferable Representations with a Single Target Domain—0
Meta-Learning Transformers to Improve In-Context Generalization—0
Meta-Learning via Feature-Label Memory Network—0
Meta-learning via Language Model In-context Tuning—0
Meta-Learning via Learned Loss—0
Meta Learning via Learned Loss—0
Meta-Learning via Learning with Distributed Memory—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