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 851860 of 3073 papers

TitleStatusHype
Memory-Based Dual Gaussian Processes for Sequential LearningCode1
Deep Active Learning with Structured Neural Depth Search0
Advancing African-Accented Speech Recognition: Epistemic Uncertainty-Driven Data Selection for Generalizable ASR ModelsCode0
Active Learning on Medical Image0
Agnostic Multi-Group Active Learning0
Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification0
CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions0
Scaling Evidence-based Instructional Design Expertise through Large Language Models0
Learning the Pareto Front Using Bootstrapped Observation Samples0
Let's Verify Step by StepCode4
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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