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

Showing 23512375 of 3073 papers

TitleStatusHype
Regional based query in graph active learningCode0
Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study0
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active LearningCode0
Batch Active Learning Using Determinantal Point ProcessesCode0
A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation0
RadGrad: Active learning with loss gradients0
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive ProcessesCode1
Deep Active Learning for Anchor User PredictionCode0
Low-resource Deep Entity Resolution with Transfer and Active Learning0
Active Generative Adversarial Network for Image Classification0
Bounded Expectation of Label Assignment: Dataset Annotation by Supervised Splitting with Bias-Reduction Techniques0
Online Active Learning of Reject Option Classifiers0
Non-Parametric Calibration for ClassificationCode0
Evaluation of Seed Set Selection Approaches and Active Learning Strategies in Predictive Coding0
Human-Machine Collaboration for Fast Land Cover Mapping0
Self-Supervised Exploration via DisagreementCode1
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower BoundsCode1
Context-driven Active and Incremental Activity Recognition0
Multi-modal Active Learning From Human Data: A Deep Reinforcement Learning Approach0
Preference-based Interactive Multi-Document SummarisationCode0
Active Deep Decoding of Linear Codes0
Diameter-based Interactive Structure Discovery0
Reliable training and estimation of variance networksCode0
Bayesian Active Learning With Abstention Feedbacks0
Data-efficient Neural Text Compression with Interactive LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified