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 2931–2940 of 3073 papers

TitleStatusHype
Challenges and Solutions for Latin Named Entity Recognition—0
Character Feature Engineering for Japanese Word Segmentation—0
Char-RNN and Active Learning for Hashtag Segmentation—0
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated Active Learning—0
Chimera: A Hybrid Machine Learning Driven Multi-Objective Design Space Exploration Tool for FPGA High-Level Synthesis—0
Class Balanced Dynamic Acquisition for Domain Adaptive Semantic Segmentation using Active Learning—0
Classification Committee for Active Deep Object Detection—0
Classification Tree-based Active Learning: A Wrapper Approach—0
Classifying and sorting cluttered piles of unknown objects with robots: a learning approach—0
CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification—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