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 2301–2350 of 3073 papers

TitleStatusHype
Active Learning Enables Extrapolation in Molecular Generative Models—0
Active Learning-Enhanced Dual Control for Angle-Only Initial Relative Orbit Determination—0
Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-based Multiple Instance Learning—0
Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes—0
Active Learning for Accurate Estimation of Linear Models—0
Active learning for affinity prediction of antibodies—0
Active Learning for Approximation of Expensive Functions with Normal Distributed Output Uncertainty—0
Active Learning for Argument Mining: A Practical Approach—0
Active Learning for Assisted Corpus Construction: A Case Study in Knowledge Discovery from Biomedical Text—0
Active Learning for Automated Visual Inspection of Manufactured Products—0
Active Learning for Autonomous Intelligent Agents: Exploration, Curiosity, and Interaction—0
Active learning for binary classification with variable selection—0
Active Learning for Black-Box Adversarial Attacks in EEG-Based Brain-Computer Interfaces—0
Active Learning for Breast Cancer Identification—0
Active Learning for Chinese Word Segmentation—0
NE-LP: Normalized Entropy and Loss Prediction based Sampling for Active Learning in Chinese Word Segmentation on EHRs—0
Active Learning for Community Detection in Stochastic Block Models—0
Active Learning for Conditional Inverse Design with Crystal Generation and Foundation Atomic Models—0
Active Learning for Contextual Search with Binary Feedbacks—0
Active Learning for Continual Learning: Keeping the Past Alive in the Present—0
Active Learning for Control-Oriented Identification of Nonlinear Systems—0
Active Learning for Coreference Resolution—0
Active Learning for Coreference Resolution—0
Active Learning for Cost-Sensitive Classification—0
Active Learning for Crowd-Sourced Databases—0
Active Learning for Deep Learning-Based Hemodynamic Parameter Estimation—0
Active Learning for Deep Neural Networks on Edge Devices—0
Active Learning for Deep Object Detection—0
Active learning for deep semantic parsing—0
Active Learning for Deep Visual Tracking—0
Active Learning for Delineation of Curvilinear Structures—0
Active Learning for Dependency Parsing by A Committee of Parsers—0
Active Learning for Dependency Parsing with Partial Annotation—0
Active learning for detection of stance components—0
Active Learning for Direct Preference Optimization—0
Active learning for distributionally robust level-set estimation—0
Active Learning for Domain Classification in a Commercial Spoken Personal Assistant—0
Active learning for efficient annotation in precision agriculture: a use-case on crop-weed semantic segmentation—0
Active learning for efficient data selection in radio-signal based positioning via deep learning—0
Active Learning for Efficient Testing of Student Programs—0
Active learning for energy-based antibody optimization and enhanced screening—0
Active learning for enumerating local minima based on Gaussian process derivatives—0
Active Learning for Event Detection in Support of Disaster Analysis Applications—0
Active Learning for Event Extraction with Memory-based Loss Prediction Model—0
Active Learning for Fair and Stable Online Allocations—0
Active learning for fast and slow modeling attacks on Arbiter PUFs—0
Active Learning for Financial Investment Reports—0
Active Learning for Fine-Grained Sketch-Based Image Retrieval—0
Active Learning for Finely-Categorized Image-Text Retrieval by Selecting Hard Negative Unpaired Samples—0
Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselage—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