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 1801–1825 of 3073 papers

TitleStatusHype
Refined Mechanism Design for Approximately Structured Priors via Active Regression—0
regAL: Python Package for Active Learning of Regression Problems—0
ReGAL: Rule-Generative Active Learning for Model-in-the-Loop Weak Supervision—0
Region-level Active Detector Learning—0
Reinforced Meta Active Learning—0
Reinforcement-based Display-size Selection for Frugal Satellite Image Change Detection—0
Reinforcement-based frugal learning for satellite image change detection—0
Reinforcement Learning Approach to Active Learning for Image Classification—0
Reinforcement Learning from Human Feedback with Active Queries—0
Relevance feedback strategies for recall-oriented neural information retrieval—0
Reliability Estimation of an Advanced Nuclear Fuel using Coupled Active Learning, Multifidelity Modeling, and Subset Simulation—0
Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors—0
A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexity—0
Removing the Training Wheels: A Coreference Dataset that Entertains Humans and Challenges Computers—0
Rényi Entropy Bounds on the Active Learning Cost-Performance Tradeoff—0
Reserved Self-training: A Semi-supervised Sentiment Classification Method for Chinese Microblogs—0
Residual Gaussian Process: A Tractable Nonparametric Bayesian Emulator for Multi-fidelity Simulations—0
Resource Aware Multifidelity Active Learning for Efficient Optimization—0
Responsible Active Learning via Human-in-the-loop Peer Study—0
Restless Bandits with Many Arms: Beating the Central Limit Theorem—0
Rethinking Crowdsourcing Annotation: Partial Annotation with Salient Labels for Multi-Label Image Classification—0
Reversed Active Learning based Atrous DenseNet for Pathological Image Classification—0
Best Practices in Active Learning for Semantic Segmentation—0
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning—0
Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces—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