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 2226–2250 of 3073 papers

TitleStatusHype
Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties—0
Interactive algorithms: from pool to stream—0
Interactive Event Sifting using Bayesian Graph Neural Networks—0
Interactive Machine Teaching by Labeling Rules and Instances—0
Interactive Ontology Matching with Cost-Efficient Learning—0
Interactive Robot Training for Non-Markov Tasks—0
Interactive Structure Learning with Structural Query-by-Committee—0
Learning to Optimize Contextually Constrained Problems for Real-Time Decision-Generation—0
Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation—0
Interpret-able feedback for AutoML systems—0
Analyzing Data Selection Techniques with Tools from the Theory of Information Losses—0
Introducing Geometry in Active Learning for Image Segmentation—0
Inverse design optimization framework via a two-step deep learning approach: application to a wind turbine airfoil—0
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling—0
Investigating Active-learning-based Training Data Selection for Speech Spoofing Countermeasure—0
Investigating Active Learning for Short-Answer Scoring—0
Investigating Active Learning in Interactive Neural Machine Translation—0
Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification—0
Investigating the Effectiveness of Representations Based on Pretrained Transformer-based Language Models in Active Learning for Labelling Text Datasets—0
ISEE.U: Distributed online active target localization with unpredictable targets—0
Is margin all you need? An extensive empirical study of active learning on tabular data—0
Is More Data Better? Using Transformers-Based Active Learning for Efficient and Effective Detection of Abusive Language—0
Is there something I'm missing? Topic Modeling in eDiscovery—0
Investigating Active Learning and Meta-Learning for Iterative Peptide Design—0
Jasmine: A New Active Learning Approach to Combat Cybercrime—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