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 801–850 of 3569 papers

TitleStatusHype
From Abstraction to Reality: DARPA's Vision for Robust Sim-to-Real Autonomy—0
Meta-learning characteristics and dynamics of quantum systems—0
Enhancing Multi-Agent Systems via Reinforcement Learning with LLM-based Planner and Graph-based Policy—0
A Hybrid Architecture with Efficient Fine Tuning for Abstractive Patent Document Summarization—0
Bridging Pattern-Aware Complexity with NP-Hard Optimization: A Unifying Framework and Empirical Study—0
DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse TasksCode0
Overlap-aware meta-learning attention to enhance hypergraph neural networks for node classification—0
PIED: Physics-Informed Experimental Design for Inverse ProblemsCode0
MetaXCR: Reinforcement-Based Meta-Transfer Learning for Cross-Lingual Commonsense Reasoning—0
Clustering-based Meta Bayesian Optimization with Theoretical Guarantee—0
Meta Learning not to Learn: Robustly Informing Meta-Learning under Nuisance-Varying Families—0
On the Convergence of Adam-Type Algorithm for Bilevel Optimization under Unbounded Smoothness—0
Meta-Learning to Explore via Memory Density Feedback—0
AugFL: Augmenting Federated Learning with Pretrained ModelsCode0
Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspection—0
Category-level Meta-learned NeRF Priors for Efficient Object Mapping—0
PABBO: Preferential Amortized Black-Box OptimizationCode0
Model-Agnostic Meta-Policy Optimization via Zeroth-Order Estimation: A Linear Quadratic Regulator Perspective—0
Teasing Apart Architecture and Initial Weights as Sources of Inductive Bias in Neural Networks—0
Online Meta-learning for AutoML in Real-time (OnMAR)—0
Learning to Generalize without Bias for Open-Vocabulary Action Recognition—0
AutoML for Multi-Class Anomaly Compensation of Sensor DriftCode0
On-device edge learning for IoT data streams: a survey—0
ToMCAT: Theory-of-Mind for Cooperative Agents in Teams via Multiagent Diffusion Policies—0
In-context learning of evolving data streams with tabular foundational models—0
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning—0
Enhancing CoMP-RSMA Performance with Movable Antennas: A Meta-Learning Optimization Framework—0
MetaSym: A Symplectic Meta-learning Framework for Physical Intelligence—0
Set a Thief to Catch a Thief: Combating Label Noise through Noisy Meta Learning—0
Dual-level Mixup for Graph Few-shot Learning with Fewer TasksCode0
AI-Driven Discovery of High Performance Polymer Electrodes for Next-Generation BatteriesCode0
A Chain-of-Thought Subspace Meta-Learning for Few-shot Image Captioning with Large Vision and Language Models—0
Uncertain Multi-Objective Recommendation via Orthogonal Meta-Learning Enhanced Bayesian Optimization—0
UniMatch: Universal Matching from Atom to Task for Few-Shot Drug DiscoveryCode0
PrivilegedDreamer: Explicit Imagination of Privileged Information for Rapid Adaptation of Learned Policies—0
Generalizable speech deepfake detection via meta-learned LoRA—0
Diverse Inference and Verification for Advanced Reasoning—0
A Survey of Reinforcement Learning for Optimization in Automation—0
A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning—0
Meta-INR: Efficient Encoding of Volumetric Data via Meta-Learning Implicit Neural RepresentationCode0
A Low-Complexity Plug-and-Play Deep Learning Model for Massive MIMO Precoding Across Sites—0
TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning—0
Analytic Personalized Federated Meta-Learning—0
Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts—0
A Meta-learner for Heterogeneous Effects in Difference-in-Differences—0
Discovering Physics Laws of Dynamical Systems via Invariant Function Learning—0
Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer—0
TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential RecommendationCode0
Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization—0
INTACT: Inducing Noise Tolerance through Adversarial Curriculum Training for LiDAR-based Safety-Critical Perception and Autonomy—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