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 2501–2525 of 3073 papers

TitleStatusHype
A Bayesian Approach for Sequence Tagging with CrowdsCode0
Work Smart - Reducing Effort in Short-Answer Grading—0
APLenty: annotation tool for creating high-quality datasets using active and proactive learning—0
Double Q-PID algorithm for mobile robot controlCode0
FrameIt: Ontology Discovery for Noisy User-Generated Text—0
Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation—0
CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation—0
Prediction of Atomization Energy Using Graph Kernel and Active Learning—0
Incremental Deep Learning for Robust Object Detection in Unknown Cluttered Environments—0
Technology Assisted Reviews: Finding the Last Few Relevant Documents by Asking Yes/No Questions to ReviewersCode0
Learning a Set of Interrelated Tasks by Using Sequences of Motor Policies for a Strategic Intrinsically Motivated Learner—0
Batch Active Preference-Based Learning of Reward FunctionsCode0
Discovering General-Purpose Active Learning StrategiesCode0
Active Learning for New Domains in Natural Language Understanding—0
Learning to Actively Learn Neural Machine Translation—0
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning—0
Visual Supervision in Bootstrapped Information Extraction—0
Using active learning to expand training data for implicit discourse relation recognitionCode0
Dynamic Ensemble Active Learning: A Non-Stationary Bandit with Expert Advice—0
Target-Independent Active Learning via Distribution-Splitting—0
Generative Adversarial Active Learning for Unsupervised Outlier DetectionCode0
An Intelligent Extraversion Analysis Scheme from Crowd Trajectories for Surveillance—0
A novel active learning framework for classification: using weighted rank aggregation to achieve multiple query criteria—0
Active Learning for Deep Object Detection—0
MedAL: Deep Active Learning Sampling Method for Medical Image Analysis—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