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 1676–1700 of 3073 papers

TitleStatusHype
Using Error Decay Prediction to Overcome Practical Issues of Deep Active Learning for Named Entity Recognition—0
PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations—0
Paladin: an annotation tool based on active and proactive learning—0
PAL : Pretext-based Active Learning—0
PANFIS++: A Generalized Approach to Evolving Learning—0
Para-active learning—0
Parallel FDA5 for Fast Deployment of Accurate Statistical Machine Translation Systems—0
Parameter-Efficient Active Learning for Foundational models—0
Parameter Filter-based Event-triggered Learning—0
Learning the Pareto Front Using Bootstrapped Observation Samples—0
Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios—0
Pareto Optimization to Accelerate Multi-Objective Virtual Screening—0
Parsimonious Dataset Construction for Laparoscopic Cholecystectomy Structure Segmentation—0
Parsimonious Random Vector Functional Link Network for Data Streams—0
PartAL: Efficient Partial Active Learning in Multi-Task Visual Settings—0
Partial-Adaptive Submodular Maximization—0
Partial-Monotone Adaptive Submodular Maximization—0
Participation in TREC 2020 COVID Track Using Continuous Active Learning—0
Parting with Illusions about Deep Active Learning—0
Partitioned Active Learning for Heterogeneous Systems—0
Passive and Active Learning of Driver Behavior from Electric Vehicles—0
Patient Aware Active Learning for Fine-Grained OCT Classification—0
Payoff Information and Learning in Signaling Games—0
Peer to Peer Learning Platform Optimized With Machine Learning—0
Perception Without Vision for Trajectory Prediction: Ego Vehicle Dynamics as Scene Representation for Efficient Active Learning in Autonomous Driving—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