SOTAVerified

Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 901–925 of 3073 papers

TitleStatusHype
You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic SegmentationCode1
Self-Correcting Bayesian Optimization through Bayesian Active Learning—0
SelfAct: Personalized Activity Recognition based on Self-Supervised and Active Learning—0
Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning—0
Prediction-Oriented Bayesian Active LearningCode1
Dynamic Exploration-Exploitation Trade-Off in Active Learning Regression with Bayesian Hierarchical Modeling—0
A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next stepsCode0
Optimizing Multi-Domain Performance with Active Learning-based Improvement Strategies—0
Does Informativeness Matter? Active Learning for Educational Dialogue Act Classification—0
OpenAL: Evaluation and Interpretation of Active Learning StrategiesCode0
Controllable Textual Inversion for Personalized Text-to-Image GenerationCode0
Deep Active Alignment of Knowledge Graph Entities and SchemataCode0
Towards Active Learning for Action Spotting in Association Football Videos—0
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction—0
A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction—0
High Accuracy Uncertainty-Aware Interatomic Force Modeling with Equivariant Bayesian Neural Networks—0
A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experimentsCode1
Incorporating Unlabelled Data into Bayesian Neural Networks—0
Creating Custom Event Data Without Dictionaries: A Bag-of-TricksCode1
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation—0
AISecKG: Knowledge Graph Dataset for Cybersecurity EducationCode1
Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costsCode0
Fairness-Aware Data Valuation for Supervised Learning—0
MuRAL: Multi-Scale Region-based Active Learning for Object Detection—0
Adaptive Superpixel for Active Learning in Semantic SegmentationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified