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 2201–2225 of 3073 papers

TitleStatusHype
Improving the quality control of seismic data through active learning—0
Improving Uncertainty Sampling with Bell Curve Weight Function—0
In Automation We Trust: Investigating the Role of Uncertainty in Active Learning Systems—0
'In-Between' Uncertainty in Bayesian Neural Networks—0
Incentive Compatible Active Learning—0
Incentivized Collaboration in Active Learning—0
Inconsistency-based Active Learning for LiDAR Object Detection—0
Incorporating Unlabeled Data into Distributionally Robust Learning—0
Incorporating Unlabelled Data into Bayesian Neural Networks—0
Increasing Interpretability of Neural Networks By Approximating Human Visual Saliency—0
Increasing Performance And Sample Efficiency With Model-agnostic Interactive Feature Attributions—0
Incremental Active Opinion Learning Over a Stream of Opinionated Documents—0
Incremental Activity Modeling and Recognition in Streaming Videos—0
Incremental Deep Learning for Robust Object Detection in Unknown Cluttered Environments—0
In Defense of Core-set: A Density-aware Core-set Selection for Active Learning—0
Indirect Active Learning—0
Influence functions and regularity tangents for efficient active learning—0
Information Gain Sampling for Active Learning in Medical Image Classification—0
Informative sample generation using class aware generative adversarial networks for classification of chest Xrays—0
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective—0
Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments—0
Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning—0
Integrating Crowdsourcing and Active Learning for Classification of Work-Life Events from Tweets—0
Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression—0
Integrating Semi-Supervised and Active Learning for Semantic Segmentation—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