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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 926–950 of 3569 papers

TitleStatusHype
Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments—0
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP—0
A Meta-GNN approach to personalized seizure detection and classification—0
EndTimes at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with BERT and Ensembles—0
Divergent Search for Few-Shot Image Classification—0
End to End Generative Meta Curriculum Learning For Medical Data Augmentation—0
End-to-End Learning of Deep Kernel Acquisition Functions for Bayesian Optimization—0
End-to-End Offline Speech Translation System for IWSLT 2020 using Modality Agnostic Meta-Learning—0
End-to-End Simultaneous Translation System for IWSLT2020 Using Modality Agnostic Meta-Learning—0
Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks—0
Enhanced Bilevel Optimization via Bregman Distance—0
Automatic tuning of hyper-parameters of reinforcement learning algorithms using Bayesian optimization with behavioral cloning—0
Enhancing CoMP-RSMA Performance with Movable Antennas: A Meta-Learning Optimization Framework—0
Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models—0
Enhancing Generalization of First-Order Meta-Learning—0
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
Enhancing Multi-Agent Systems via Reinforcement Learning with LLM-based Planner and Graph-based Policy—0
A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics—0
A Meta Understanding of Meta-Learning—0
Few-shot Learning with Meta Metric Learners—0
Distribution Embedding Network for Meta-Learning with Variable-Length Input—0
ERMAS: Learning Policies Robust to Reality Gaps in Multi-Agent Simulations—0
ES-Based Jacobian Enables Faster Bilevel Optimization—0
Distributionally robust minimization in meta-learning for system identification—0
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition—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