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 13311340 of 3073 papers

TitleStatusHype
Deterministic Langevin Unconstrained Optimization with Normalizing Flows0
Confidence Adjusted Surprise Measure for Active Resourceful Trials (CA-SMART): A Data-driven Active Learning Framework for Accelerating Material Discovery under Resource Constraints0
AdaptiFont: Increasing Individuals' Reading Speed with a Generative Font Model and Bayesian Optimization0
DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measures0
ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and Response with AI0
Dialog Policy Learning for Joint Clarification and Active Learning Queries0
Diameter-Based Active Learning0
Diameter-based Interactive Structure Discovery0
Differentiable Submodular Maximization0
Concurrent Active Learning in Autonomous Airborne Source Search: Dual Control for Exploration and Exploitation0
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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