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 951–975 of 3569 papers

TitleStatusHype
FewShotNeRF: Meta-Learning-based Novel View Synthesis for Rapid Scene-Specific Adaptation—0
Few-Shot Regression via Learned Basis Functions—0
Evaluated CMI Bounds for Meta Learning: Tightness and Expressiveness—0
Evaluating Data Influence in Meta Learning—0
Distribution Embedding Network for Meta-Learning with Variable-Length Input—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
Task-Robust Model-Agnostic Meta-Learning—0
Distributed Representations of Words and Documents for Discriminating Similar Languages—0
A meta-algorithm for classification using random recursive tree ensembles: A high energy physics application—0
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks—0
EvoFA: Evolvable Fast Adaptation for EEG Emotion Recognition—0
A Communication and Computation Efficient Fully First-order Method for Decentralized Bilevel Optimization—0
Evolution of Efficient Symbolic Communication Codes—0
Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks—0
Evolving Domain Generalization—0
Evolving Machine Learning: A Survey—0
Evolving parametrized Loss for Image Classification Learning on Small Datasets—0
Parallel Momentum Methods Under Biased Gradient Estimations—0
Automatic low-bit hybrid quantization of neural networks through meta learning—0
Boosting Generalizability towards Zero-Shot Cross-Dataset Single-Image Indoor Depth by Meta-Initialization—0
Expert Training: Task Hardness Aware Meta-Learning for Few-Shot Classification—0
Explaining the Performance of Multi-label Classification Methods with Data Set Properties—0
Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning—0
Distributed Evolution Strategies Using TPUs for Meta-Learning—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