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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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 2361–2370 of 3073 papers

Title	Date	Tasks	Status	Hype
Bounded Expectation of Label Assignment: Dataset Annotation by Supervised Splitting with Bias-Reduction Techniques	Jun 17, 2019	Active LearningClustering	—Unverified	0
Online Active Learning of Reject Option Classifiers	Jun 14, 2019	Active LearningBinary Classification	—Unverified	0
Non-Parametric Calibration for Classification	Jun 12, 2019	Active LearningClassification	CodeCode Available	0
Evaluation of Seed Set Selection Approaches and Active Learning Strategies in Predictive Coding	Jun 11, 2019	Active LearningClustering	—Unverified	0
Human-Machine Collaboration for Fast Land Cover Mapping	Jun 10, 2019	Active Learning	—Unverified	0
Self-Supervised Exploration via Disagreement	Jun 10, 2019	Active LearningEfficient Exploration	CodeCode Available	1
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds	Jun 9, 2019	Active LearningDiversity	CodeCode Available	1
Context-driven Active and Incremental Activity Recognition	Jun 7, 2019	Active LearningActivity Recognition	—Unverified	0
Multi-modal Active Learning From Human Data: A Deep Reinforcement Learning Approach	Jun 7, 2019	Active LearningDeep Reinforcement Learning	—Unverified	0
Preference-based Interactive Multi-Document Summarisation	Jun 7, 2019	Active Learningreinforcement-learning	CodeCode Available	0

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Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	TypiClust	Accuracy	93.2	—	Unverified
2	PT4AL	Accuracy	93.1	—	Unverified
3	Learning loss	Accuracy	91.01	—	Unverified
4	CoreGCN	Accuracy	90.7	—	Unverified
5	Core-set	Accuracy	89.92	—	Unverified
6	Random Baseline (Resnet18)	Accuracy	88.45	—	Unverified
7	Random Baseline (VGG16)	Accuracy	85.09	—	Unverified