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 1951–1975 of 3073 papers

TitleStatusHype
Cost-Based Budget Active Learning for Deep Learning—0
Active Learning: Problem Settings and Recent Developments—0
A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design—0
Perfect density models cannot guarantee anomaly detection—0
Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval—0
Bayesian Active Learning for Wearable Stress and Affect Detection—0
Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning—0
Sparse Semi-Supervised Action Recognition with Active Learning—0
Stochastic Adversarial Gradient Embedding for Active Domain Adaptation—0
Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning—0
Deep Multi-Fidelity Active Learning of High-dimensional Outputs—0
High-contrast “gaudy” images improve the training of deep neural network models of visual cortexCode0
CORA: A Deep Active Learning Covid-19 Relevancy Algorithm to Identify Core Scientific Articles—0
Constructing a Korean Named Entity Recognition Dataset for the Financial Domain using Active Learning—0
Bilingual Transfer Learning for Online Product Classification—0
Uncertainty Modeling for Machine Comprehension Systems using Efficient Bayesian Neural Networks—0
A Multitask Active Learning Framework for Natural Language Understanding—0
Enhanced Labelling in Active Learning for Coreference Resolution—0
Variance based sensitivity analysis for Monte Carlo and importance sampling reliability assessment with Gaussian processes—0
On Initial Pools for Deep Active LearningCode0
Active Output Selection Strategies for Multiple Learning Regression Models—0
A smartphone based multi input workflow for non-invasive estimation of haemoglobin levels using machine learning techniques—0
Deep Active Learning for Sequence Labeling Based on Diversity and Uncertainty in Gradient—0
Active Learning in CNNs via Expected Improvement MaximizationCode0
Low-Resolution Face Recognition In Resource-Constrained Environments—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