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 331340 of 3073 papers

TitleStatusHype
dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple HumansCode1
On the Pros and Cons of Active Learning for Moral Preference Elicitation0
Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual PersistenceCode0
Amortized Active Learning for Nonparametric Functions0
A Novel Two-Step Fine-Tuning Pipeline for Cold-Start Active Learning in Text Classification Tasks0
Self-driving lab discovers principles for steering spontaneous emission0
Exploring and Addressing Reward Confusion in Offline Preference Learning0
MILAN: Milli-Annotations for Lidar Semantic Segmentation0
Enhancing Retinal Disease Classification from OCTA Images via Active Learning TechniquesCode0
Downstream-Pretext Domain Knowledge Traceback for Active Learning0
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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