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 2976–3000 of 3073 papers

TitleStatusHype
Concurrent Active Learning in Autonomous Airborne Source Search: Dual Control for Exploration and Exploitation—0
Confidence Adjusted Surprise Measure for Active Resourceful Trials (CA-SMART): A Data-driven Active Learning Framework for Accelerating Material Discovery under Resource Constraints—0
Confidence-based Active Learning Methods for Machine Translation—0
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout—0
Confidence Decision Trees via Online and Active Learning for Streaming (BIG) Data—0
Confidence Estimation for Object Detection in Document Images—0
Confident Coreset for Active Learning in Medical Image Analysis—0
ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion—0
Congruence-based Learning of Probabilistic Deterministic Finite Automata—0
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Budget—0
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost—0
Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification—0
Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions—0
Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks—0
Constrained Bayesian Optimization with Adaptive Active Learning of Unknown Constraints—0
Constrained Online Decision-Making: A Unified Framework—0
Constructing a Korean Named Entity Recognition Dataset for the Financial Domain using Active Learning—0
Containing a spread through sequential learning: to exploit or to explore?—0
Context Aware Active Learning of Activity Recognition Models—0
Context-aware Active Multi-Step Reinforcement Learning—0
Context Aware Image Annotation in Active Learning—0
Context-Aware Query Selection for Active Learning in Event Recognition—0
Context-driven Active and Incremental Activity Recognition—0
Contextual Bayesian optimization with binary outputs—0
Multi-View Active Learning for Short Text Classification in User-Generated Data—0
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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