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 2401–2450 of 3569 papers

TitleStatusHype
A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism—0
A Simple Recipe to Meta-Learn Forward and Backward Transfer—0
Supervised Graph Contrastive Learning for Few-shot Node Classification—0
A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm—0
A Single-Timescale Method for Stochastic Bilevel Optimization—0
Assessing two novel distance-based loss functions for few-shot image classification—0
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation—0
A statistical physics framework for optimal learning—0
A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning—0
A Student-Teacher Architecture for Dialog Domain Adaptation under the Meta-Learning Setting—0
A study on Ensemble Learning for Time Series Forecasting and the need for Meta-Learning—0
A supervised generative optimization approach for tabular data—0
A Survey of Deep Meta-Learning—0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
A Survey of Reinforcement Learning for Optimization in Automation—0
A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?—0
A Survey on Few-Shot Class-Incremental Learning—0
A Survey on Knowledge Editing of Neural Networks—0
A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior—0
A Systematic Review of Few-Shot Learning in Medical Imaging—0
A Theoretical Analysis of the Number of Shots in Few-Shot Learning—0
A Thorough Review on Recent Deep Learning Methodologies for Image Captioning—0
Attacking Few-Shot Classifiers with Adversarial Support Sets—0
Attacking Few-Shot Classifiers with Adversarial Support Poisoning—0
Attentional Graph Meta-Learning for Indoor Localization Using Extremely Sparse Fingerprints—0
Attention-based Few-Shot Person Re-identification Using Meta Learning—0
Attentive Feature Reuse for Multi Task Meta learning—0
Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification—0
Attribute-Modulated Generative Meta Learning for Zero-Shot Classification—0
Attribute Propagation Network for Graph Zero-shot Learning—0
Audio Anti-spoofing Using a Simple Attention Module and Joint Optimization Based on Additive Angular Margin Loss and Meta-learning—0
Augmentation Learning for Semi-Supervised Classification—0
Augmenting Medical Imaging: A Comprehensive Catalogue of 65 Techniques for Enhanced Data Analysis—0
Augmenting Supervised Learning by Meta-learning Unsupervised Local Rules—0
A Unified Framework with Meta-dropout for Few-shot Learning—0
A Universal Knowledge Model and Cognitive Architecture for Prototyping AGI—0
Auto-CASH: Autonomous Classification Algorithm Selection with Deep Q-Network—0
AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting—0
AutoLoRA: Automatically Tuning Matrix Ranks in Low-Rank Adaptation Based on Meta Learning—0
AutoLoss: Learning Discrete Schedule for Alternate Optimization—0
Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence—0
Automated Few-Shot Time Series Forecasting based on Bi-level Programming—0
Automated Grading of Students' Handwritten Graphs: A Comparison of Meta-Learning and Vision-Large Language Models—0
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems—0
Automatic Combination of Sample Selection Strategies for Few-Shot Learning—0
Automatic Forecasting via Meta-Learning—0
Automatic learning algorithm selection for classification via convolutional neural networks—0
Automatic Learning to Detect Concept Drift—0
Automatic low-bit hybrid quantization of neural networks through meta learning—0
Automatic tuning of hyper-parameters of reinforcement learning algorithms using Bayesian optimization with behavioral cloning—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