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 931940 of 3073 papers

TitleStatusHype
A Competitive Algorithm for Agnostic Active Learning0
Learning to Rank for Active Learning via Multi-Task Bilevel Optimization0
MyriadAL: Active Few Shot Learning for HistopathologyCode0
Efficient Active Learning Halfspaces with Tsybakov Noise: A Non-convex Optimization Approach0
Turn-Level Active Learning for Dialogue State TrackingCode0
Making RL with Preference-based Feedback Efficient via Randomization0
ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation0
Bayesian Active Learning in the Presence of Nuisance Parameters0
MeaeQ: Mount Model Extraction Attacks with Efficient QueriesCode0
Cache & Distil: Optimising API Calls to Large Language Models0
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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