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 1401–1425 of 3073 papers

TitleStatusHype
Distribution-Dependent Sample Complexity of Large Margin Learning—0
Downstream-Pretext Domain Knowledge Traceback for Active Learning—0
Do you Feel Certain about your Annotation? A Web-based Semantic Frame Annotation Tool Considering Annotators' Concerns and Behaviors—0
DP-Dueling: Learning from Preference Feedback without Compromising User Privacy—0
An Active Learning Framework for Efficient Robust Policy Search—0
DroidStar: Callback Typestates for Android Classes—0
Distribution Aware Active Learning—0
Distributional Term Set Expansion—0
An Active Learning Framework for Constructing High-fidelity Mobility Maps—0
Distributionally Robust Statistical Verification with Imprecise Neural Networks—0
Dual Adversarial Network for Deep Active Learning—0
Dual Control of Exploration and Exploitation for Auto-Optimisation Control with Active Learning—0
Distributionally Robust Active Learning for Gaussian Process Regression—0
DutchSemCor: Targeting the ideal sense-tagged corpus—0
Dynamic Ensemble Active Learning: A Non-Stationary Bandit with Expert Advice—0
Dynamic Exploration-Exploitation Trade-Off in Active Learning Regression with Bayesian Hierarchical Modeling—0
Early Forecasting of Text Classification Accuracy and F-Measure with Active Learning—0
EASE: An Easily-Customized Annotation System Powered by Efficiency Enhancement Mechanisms—0
Easy Questions First? A Case Study on Curriculum Learning for Question Answering—0
ED2: Two-stage Active Learning for Error Detection -- Technical Report—0
Distributional Latent Variable Models with an Application in Active Cognitive Testing—0
Edge-guided and Class-balanced Active Learning for Semantic Segmentation of Aerial Images—0
Active Learning in Incomplete Label Multiple Instance Multiple Label Learning—0
Active Community Detection with Maximal Expected Model Change—0
ACIL: Active Class Incremental Learning for 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