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 3001–3025 of 3073 papers

TitleStatusHype
Towards a Tool for Interactive Concept Building for Large Scale Analysis in the Humanities—0
Impact of ASR N-Best Information on Bayesian Dialogue Act Recognition—0
SHEF-Lite: When Less is More for Translation Quality Estimation—0
Online Active Learning for Cost Sensitive Domain Adaptation—0
Annotating named entities in clinical text by combining pre-annotation and active learning—0
Reducing Annotation Effort for Quality Estimation via Active Learning—0
Text Classification from Positive and Unlabeled Data using Misclassified Data Correction—0
The Power of Localization for Efficiently Learning Linear Separators with Noise—0
Statistical Active Learning Algorithms for Noise Tolerance and Differential Privacy—0
Auditing: Active Learning with Outcome-Dependent Query Costs—0
Evaluating Unsupervised Language Model Adaptation Methods for Speaking Assessment—0
NIL\_UCM: Extracting Drug-Drug interactions from text through combination of sequence and tree kernels—0
Optimal Data Set Selection: An Application to Grapheme-to-Phoneme Conversion—0
Adaptive Active Learning for Image Classification—0
Simultaneous Active Learning of Classifiers & Attributes via Relative Feedback—0
Machine learning of hierarchical clustering to segment 2D and 3D imagesCode0
Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in RobotsCode0
Active Learning and the Irish Treebank—0
CRAB Reader: A Tool for Analysis and Visualization of Argumentative Zones in Scientific Literature—0
Active Learning for Chinese Word Segmentation—0
Online allocation and homogeneous partitioning for piecewise constant mean-approximation—0
Bayesian active learning with localized priors for fast receptive field characterization—0
Hierarchical Optimistic Region Selection driven by Curiosity—0
A Linear Time Active Learning Algorithm for Link Classification—0
Collaborative Gaussian Processes for Preference Learning—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