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 1851–1900 of 3569 papers

TitleStatusHype
Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles—0
Negative Inner-Loop Learning Rates Learn Universal Features—0
Network bottlenecks and task structure control the evolution of interpretable learning rules in a foraging agent—0
Neural Algorithms for Graph Navigation—0
Neural Collaborative Filtering Bandits via Meta Learning—0
Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning—0
Neural Variational Dropout Processes—0
NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results—0
Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning—0
Neuromorphic on-chip reservoir computing with spiking neural network architectures—0
Next-item Recommendations in Short Sessions—0
Noether Networks: Meta-Learning Useful Conserved Quantities—0
Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings—0
Non-greedy Gradient-based Hyperparameter Optimization Over Long Horizons—0
Nonlinear Meta-Learning Can Guarantee Faster Rates—0
Nonstationary Nonparametric Online Learning: Balancing Dynamic Regret and Model Parsimony—0
Nonstochastic Bandits with Infinitely Many Experts—0
NormGrad: Finding the Pixels that Matter for Training—0
NORML: Nodal Optimization for Recurrent Meta-Learning—0
NoRML: No-Reward Meta Learning—0
Not All Instances Contribute Equally: Instance-adaptive Class Representation Learning for Few-Shot Visual Recognition—0
Not All Negatives Are Worth Attending to: Meta-Bootstrapping Negative Sampling Framework for Link Prediction—0
Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video—0
On Finding Small Hyper-Gradients in Bilevel Optimization: Hardness Results and Improved Analysis—0
On Data Efficiency of Meta-learning—0
On-device edge learning for IoT data streams: a survey—0
One-at-a-time: A Meta-Learning Recommender-System for Recommendation-Algorithm Selection on Micro Level—0
One-Class Domain Adaptation via Meta-Learning—0
One-Class Meta-Learning: Towards Generalizable Few-Shot Open-Set Classification—0
On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation—0
One-shot Imitation in a Non-Stationary Environment via Multi-Modal Skill—0
One-Shot Imitation Learning—0
One to Many: Adaptive Instrument Segmentation via Meta Learning and Dynamic Online Adaptation in Robotic Surgical Video—0
On Extending Neural Networks with Loss Ensembles for Text Classification—0
On Hard Episodes in Meta-Learning—0
On Implicit Bias in Overparameterized Bilevel Optimization—0
On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image Classifiers—0
Online Adaptation through Meta-Learning for Stereo Depth Estimation—0
Online Algorithms for Hierarchical Inference in Deep Learning applications at the Edge—0
Online Bayesian Meta-Learning for Cognitive Tracking Radar—0
Online Continual Learning via the Meta-learning Update with Multi-scale Knowledge Distillation and Data Augmentation—0
Online Depth Learning Against Forgetting in Monocular Videos—0
Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning—0
Reconciling meta-learning and continual learning with online mixtures of tasks—0
Online Hyperparameter Meta-Learning with Hypergradient Distillation—0
Online Loss Function Learning—0
Online Meta Adaptation for Variable-Rate Learned Image Compression—0
Online Meta-Learning—0
Online Meta-learning by Parallel Algorithm Competition—0
Online Meta-Learning Channel Autoencoder for Dynamic End-to-end Physical Layer Optimization—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