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 2051–2100 of 3569 papers

TitleStatusHype
Min-Max Bilevel Multi-objective Optimization with Applications in Machine LearningCode0
Continuous-Time Meta-Learning with Forward Mode Differentiation—0
A Primal-Dual Approach to Bilevel Optimization with Multiple Inner Minima—0
Prepare for Trouble and Make it Double. Supervised and Unsupervised Stacking for AnomalyBased Intrusion Detection—0
Amortized Proximal Optimization—0
Interpretable Concept-based Prototypical Networks for Few-Shot Learning—0
Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework—0
An Unsupervised Cross-Modal Hashing Method Robust to Noisy Training Image-Text Correspondences in Remote SensingCode0
Meta-Learning for Simple Regret MinimizationCode0
MetaVA: Curriculum Meta-learning and Pre-fine-tuning of Deep Neural Networks for Detecting Ventricular Arrhythmias based on ECGs—0
Non-stationary Bandits and Meta-Learning with a Small Set of Optimal ArmsCode0
Towards Better Meta-Initialization with Task Augmentation for Kindergarten-aged Speech Recognition—0
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications—0
Reinforcement Learning in Practice: Opportunities and Challenges—0
Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search—0
Enabling Reproducibility and Meta-learning Through a Lifelong Database of Experiments (LDE)—0
Imbalanced Classification via Explicit Gradient Learning From Augmented Data—0
A History of Meta-gradient: Gradient Methods for Meta-learning—0
Trace norm regularization for multi-task learning with scarce dataCode0
Meta-learning with GANs for anomaly detection, with deployment in high-speed rail inspection system—0
Adaptive Mixing of Auxiliary Losses in Supervised LearningCode0
MAML and ANIL Provably Learn Representations—0
Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks—0
FORML: Learning to Reweight Data for Fairness—0
Meta-Learning Hypothesis Spaces for Sequential Decision-making—0
Fully Online Meta-Learning Without Task Boundaries—0
Neural Collaborative Filtering Bandits via Meta Learning—0
PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual InformationCode0
Deep Task-Based Analog-to-Digital ConversionCode0
Generalized Visual Quality Assessment of GAN-Generated Face Images—0
Transfering Hierarchical Structure with Dual Meta Imitation Learning—0
The Effect of Diversity in Meta-LearningCode0
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task RepresentationCode0
Modeling Human Exploration Through Resource-Rational Reinforcement LearningCode0
Meta-learning Spiking Neural Networks with Surrogate Gradient DescentCode0
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images—0
Pre-Trained Language Transformers are Universal Image Classifiers—0
Synthetic speech detection using meta-learning with prototypical loss—0
Meta Learning MDPs with Linear Transition Models—0
Cross-Domain Few-Shot Graph ClassificationCode0
Fine-Grained Trajectory-based Travel Time Estimation for Multi-city Scenarios Based on Deep Meta-LearningCode0
Meta Learning for Code Summarization—0
Learning Tensor Representations for Meta-Learning—0
System-Agnostic Meta-Learning for MDP-based Dynamic Scheduling via Descriptive Policy—0
Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling—0
Representation Change in Model-Agnostic Meta-Learning—0
Prior Knowledge for Few-shot Learning—Inductive Reasoning and Distribution Calibration—0
Understanding Few-Shot Multi-Task Representation Learning Theory—0
Meta Learning for Natural Language Processing: A Survey—0
AllWOZ: Towards Multilingual Task-Oriented Dialog Systems for All—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