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
BayesOpt: A Library for Bayesian optimization with Robotics Applications—0
Active Learning for Phenotyping Tasks—0
Annotating named entities in clinical text by combining pre-annotation and active learning—0
Towards a Tool for Interactive Concept Building for Large Scale Analysis in the Humanities—0
Online Active Learning for Cost Sensitive Domain Adaptation—0
SHEF-Lite: When Less is More for Translation Quality Estimation—0
Text Classification from Positive and Unlabeled Data using Misclassified Data Correction—0
Impact of ASR N-Best Information on Bayesian Dialogue Act Recognition—0
Reducing Annotation Effort for Quality Estimation via Active Learning—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
NIL\_UCM: Extracting Drug-Drug interactions from text through combination of sequence and tree kernels—0
Evaluating Unsupervised Language Model Adaptation Methods for Speaking Assessment—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
A Linear Time Active Learning Algorithm for Link Classification—0
Collaborative Gaussian Processes for Preference Learning—0
Active Learning of Model Evidence Using Bayesian Quadrature—0
Hierarchical Optimistic Region Selection driven by Curiosity—0
Active Learning of Multi-Index Function Models—0
Active Learning for Chinese Word 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