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 1901–1950 of 3569 papers

TitleStatusHype
LEA: Meta Knowledge-Driven Self-Attentive Document Embedding for Few-Shot Text Classification—0
Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling—0
Customized Conversational Recommender Systems—0
Meta-Learning over Time for Destination Prediction Tasks—0
Dual Attention Networks for Few-Shot Fine-Grained RecognitionCode0
Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction—0
Leveraging Language for Accelerated Learning of Tool Manipulation—0
Understanding Benign Overfitting in Gradient-Based Meta Learning—0
p-Meta: Towards On-device Deep Model Adaptation—0
Multi-Access Point Coordination for Next-Gen Wi-Fi Networks Aided by Deep Reinforcement Learning—0
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks—0
Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Space—0
EEML: Ensemble Embedded Meta-learning—0
Motley: Benchmarking Heterogeneity and Personalization in Federated LearningCode0
MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-LearningCode0
Provable Generalization of Overparameterized Meta-learning Trained with SGD—0
Fast Finite Width Neural Tangent KernelCode0
Principled Acceleration of Iterative Numerical Methods Using Machine Learning—0
On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation—0
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification—0
On Provably Robust Meta-Bayesian OptimizationCode0
Category-Agnostic 6D Pose Estimation with Conditional Neural Processes—0
Faster Optimization-Based Meta-Learning Adaptation Phase—0
A General framework for PAC-Bayes Bounds for Meta-Learning—0
Lightweight Conditional Model Extrapolation for Streaming Data under Class-Prior ShiftCode0
Learning to generate imaginary tasks for improving generalization in meta-learning—0
Comprehensive Fair Meta-learned Recommender SystemCode0
Deep Meta-learning in Recommendation Systems: A Survey—0
Reconsidering Learning Objectives in Unbiased Recommendation with Unobserved Confounders—0
Meta-Learning Parameterized SkillsCode0
A Benchmark for Federated Hetero-Task Learning—0
On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning—0
Stacked unsupervised learning with a network architecture found by supervised meta-learning—0
A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism—0
Robust Meta-learning with Sampling Noise and Label Noise via Eigen-ReptileCode0
MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical ImagingCode0
Dynamic Kernel Selection for Improved Generalization and Memory Efficiency in Meta-learningCode0
On the Generalization of Neural Combinatorial Optimization Heuristics—0
Dataset Distillation using Neural Feature RegressionCode0
Meta-SysId: A Meta-Learning Approach for Simultaneous Identification and Prediction—0
Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networksCode0
FHIST: A Benchmark for Few-shot Classification of Histological Images—0
Evolving Domain Generalization—0
Online Meta-Learning in Adversarial Multi-Armed Bandits—0
Automatic Short Math Answer Grading via In-context Meta-learningCode0
GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization—0
Meta Representation Learning with Contextual Linear Bandits—0
Comparison of meta-learners for estimating multi-valued treatment heterogeneous effectsCode0
Deep Learning with Label Noise: A Hierarchical Approach—0
Meta-Learning Adversarial Bandits—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