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 526–550 of 3073 papers

TitleStatusHype
Active Deep Learning Attacks under Strict Rate Limitations for Online API Calls—0
Active Learning for Autonomous Intelligent Agents: Exploration, Curiosity, and Interaction—0
Active Learning of Sequential Transducers with Side Information about the Domain—0
Active Deep Kernel Learning of Molecular Functionalities: Realizing Dynamic Structural Embeddings—0
Active Learning of Quantum System Hamiltonians yields Query Advantage—0
Active Learning of Piecewise Gaussian Process Surrogates—0
Active Learning for Automated Visual Inspection of Manufactured Products—0
A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching—0
Bayesian Active Learning for Semantic Segmentation—0
Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection—0
Adversarial Sampling for Active Learning—0
Active learning for structural reliability: survey, general framework and benchmark—0
Algorithmic Connections Between Active Learning and Stochastic Convex Optimization—0
Active Learning of Ordinal Embeddings: A User Study on Football Data—0
Active learning of neural response functions with Gaussian processes—0
Active Learning for Assisted Corpus Construction: A Case Study in Knowledge Discovery from Biomedical Text—0
Active learning of neural population dynamics using two-photon holographic optogenetics—0
Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations—0
Active Deep Densely Connected Convolutional Network for Hyperspectral Image Classification—0
A Competitive Algorithm for Agnostic Active Learning—0
Active Learning of Multi-Index Function Models—0
Active Learning for Argument Mining: A Practical Approach—0
Active Deep Decoding of Linear Codes—0
Active Learning of Model Evidence Using Bayesian Quadrature—0
Active Learning of Mealy Machines with Timers—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