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 1651–1675 of 3073 papers

TitleStatusHype
ActiveEA: Active Learning for Neural Entity AlignmentCode0
Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning—0
AutoNLU: Detecting, root-causing, and fixing NLU model errors—0
Real-Time Learning from An Expert in Deep Recommendation Systems with Marginal Distance Probability Distribution—0
Active Altruism Learning and Information Sufficiency for Autonomous Driving—0
Bayesian Active Summarization—0
Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up—0
Active learning for interactive satellite image change detection—0
Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations—0
Synthesizing Video Trajectory Queries—0
Addressing practical challenges in Active Learning via a hybrid query strategy—0
Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-Supervised Learning—0
ActiveMatch: End-to-end Semi-supervised Active Representation Learning—0
Active Learning for Contextual Search with Binary Feedbacks—0
Automated Seed Quality Testing System using GAN & Active LearningCode0
OPAD: An Optimized Policy-based Active Learning Framework for Document Content Analysis—0
Graph-boosted Active Learning for Multi-Source Entity ResolutionCode0
Robust Segmentation Models using an Uncertainty Slice Sampling Based Annotation Workflow—0
Is Policy Learning Overrated?: Width-Based Planning and Active Learning for AtariCode0
Active Learning: Sampling in the Least Probable Disagreement Region—0
Understanding the Success of Knowledge Distillation -- A Data Augmentation Perspective—0
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach—0
ACTIVE REFINEMENT OF WEAKLY SUPERVISED MODELS—0
Best Practices in Pool-based Active Learning for Image Classification—0
SABAL: Sparse Approximation-based Batch Active 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