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 301–325 of 3073 papers

TitleStatusHype
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive ProcessesCode1
Self-Supervised Exploration via DisagreementCode1
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower BoundsCode1
Learning Loss for Active LearningCode1
Variational Adversarial Active LearningCode1
Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active LearningCode1
Prompsit's submission to WMT 2018 Parallel Corpus Filtering shared taskCode1
Active Anomaly Detection via EnsemblesCode1
OBOE: Collaborative Filtering for AutoML Model SelectionCode1
Bayesian Model-Agnostic Meta-LearningCode1
Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation EffortsCode1
Active Learning for Convolutional Neural Networks: A Core-Set ApproachCode1
Deep Active Learning for Named Entity RecognitionCode1
A Tutorial on Thompson SamplingCode1
Closing the Loop: Fast, Interactive Semi-Supervised Annotation With Queries on Features and InstancesCode1
Building a Scalable and Interpretable Bayesian Deep Learning Framework for Quality Control of Free Form SurfacesCode1
A Risk-Aware Adaptive Robust MPC with Learned Uncertainty Quantification—0
MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials—0
Active Learning for Manifold Gaussian Process RegressionCode0
Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization—0
Active Learning-Guided Seq2Seq Variational Autoencoder for Multi-target Inhibitor Generation—0
Bayesian Active Learning of (small) Quantile Sets through Expected Estimator Modification—0
Coupled reaction and diffusion governing interface evolution in solid-state batteries—0
GRAIL: A Benchmark for GRaph ActIve Learning in Dynamic Sensing Environments—0
Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems—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