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

Showing 17511760 of 3073 papers

TitleStatusHype
Probabilistic Dalek -- Emulator framework with probabilistic prediction for supernova tomography0
Probabilistic Deep Learning for Instance Segmentation0
Probabilistic Inference for Structural Health Monitoring: New Modes of Learning from Data0
Probabilistic Model-Agnostic Meta-Learning0
Probability trees and the value of a single intervention0
Probing Difficulty and Discrimination of Natural Language Questions With Item Response Theory0
Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway Segmentation from HRCT via a Human-AI Collaboration Based Active Learning Method0
ProCoT: Stimulating Critical Thinking and Writing of Students through Engagement with Large Language Models (LLMs)0
ProductNet: a Collection of High-Quality Datasets for Product Representation Learning0
Projection based Active Gaussian Process Regression for Pareto Front Modeling0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified