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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1626–1650 of 3073 papers

TitleStatusHype
Active Learning for Saddle Point Calculation—0
Probabilistic Active Learning for Active Class Selection—0
Reducing Annotating Load: Active Learning with Synthetic Images in Surgical Instrument SegmentationCode0
Active Learning of Driving Scenario Trajectories—0
Self-supervised optimization of random material microstructures in the small-data regimeCode0
Active Curriculum Learning—0
Investigating Active Learning in Interactive Neural Machine Translation—0
Subsequence Based Deep Active Learning for Named Entity Recognition—0
The University of Arizona at SemEval-2021 Task 10: Applying Self-training, Active Learning and Data Augmentation to Source-free Domain Adaptation—0
Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence MaximizationCode0
When Deep Learners Change Their Mind: Learning Dynamics for Active Learning—0
Active Learning in Gaussian Process State Space Model—0
Batch Active Learning at ScaleCode0
Semi-Supervised Active Learning with Temporal Output DiscrepancyCode1
Self-learning Emulators and Eigenvector Continuation—0
Robust and Active Learning for Deep Neural Network Regression—0
Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning—0
ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic SegmentationCode1
Restless Bandits with Many Arms: Beating the Central Limit Theorem—0
MCDAL: Maximum Classifier Discrepancy for Active LearningCode0
Robust Adaptive Submodular Maximization—0
Active Learning in Incomplete Label Multiple Instance Multiple Label Learning—0
Small-Text: Active Learning for Text Classification in Python—0
Offline Preference-Based Apprenticeship Learning—0
Fully Automated Machine Learning Pipeline for Echocardiogram Segmentation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified