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 681690 of 3073 papers

TitleStatusHype
Making RL with Preference-based Feedback Efficient via Randomization0
ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation0
A comprehensive survey on deep active learning in medical image analysisCode1
MeaeQ: Mount Model Extraction Attacks with Efficient QueriesCode0
Cache & Distil: Optimising API Calls to Large Language Models0
A Finite-Horizon Approach to Active Level Set Estimation0
An active learning convolutional neural network for predicting river flow in a human impacted systemCode0
Active Learning Framework for Cost-Effective TCR-Epitope Binding Affinity PredictionCode0
Pareto Optimization to Accelerate Multi-Objective Virtual Screening0
Open-CRB: Towards Open World Active Learning for 3D Object DetectionCode1
Show:102550
← PrevPage 69 of 308Next →

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