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 2341–2350 of 3073 papers

TitleStatusHype
Active Learning within Constrained Environments through Imitation of an Expert Questioner—0
The Practical Challenges of Active Learning: Lessons Learned from Live Experimentation—0
L*-Based Learning of Markov Decision Processes (Extended Version)—0
'In-Between' Uncertainty in Bayesian Neural Networks—0
Deep Active Learning with Adaptive AcquisitionCode0
Selection via Proxy: Efficient Data Selection for Deep LearningCode0
A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree—0
Active Learning Solution on Distributed Edge Computing—0
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout—0
Flattening a Hierarchical Clustering through Active Learning—0
Show:102550
← PrevPage 235 of 308Next →

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