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
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
Prompsit's submission to WMT 2018 Parallel Corpus Filtering shared taskCode1
Learning to Actively Learn Neural Machine Translation—0
Using active learning to expand training data for implicit discourse relation recognitionCode0
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning—0
Visual Supervision in Bootstrapped Information Extraction—0
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
Efficient Seismic fragility curve estimation by Active Learning on Support Vector Machines—0
MedAL: Deep Active Learning Sampling Method for Medical Image Analysis—0
Vis-DSS: An Open-Source toolkit for Visual Data Selection and SummarizationCode0
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems—0
Active Anomaly Detection via EnsemblesCode1
Events Beyond ACE: Curated Training for Events—0
Query-Efficient Black-Box Attack by Active Learning—0
Bayesian Semi-supervised Learning with Graph Gaussian ProcessesCode0
Physics-Information-Aided Kriging: Constructing Covariance Functions using Stochastic Simulation Models—0
Learning Time Dependent Choice—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