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 25012510 of 3073 papers

TitleStatusHype
Learning a Set of Interrelated Tasks by Using Sequences of Motor Policies for a Strategic Intrinsically Motivated Learner0
Batch Active Preference-Based Learning of Reward FunctionsCode0
Discovering General-Purpose Active Learning StrategiesCode0
Active Learning for New Domains in Natural Language Understanding0
Prompsit's submission to WMT 2018 Parallel Corpus Filtering shared taskCode1
Learning to Actively Learn Neural Machine Translation0
Using active learning to expand training data for implicit discourse relation recognitionCode0
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning0
Visual Supervision in Bootstrapped Information Extraction0
Dynamic Ensemble Active Learning: A Non-Stationary Bandit with Expert Advice0
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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