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 1876–1900 of 3073 papers

TitleStatusHype
Learning active learning at the crossroads? evaluation and discussion—0
Active Learning for Deep Gaussian Process SurrogatesCode0
Chernoff Sampling for Active Testing and Extension to Active Regression—0
Deep Bayesian Active Learning, A Brief Survey on Recent Advances—0
CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert LinkingCode1
Active Hierarchical Imitation and Reinforcement Learning—0
LSCALE: Latent Space Clustering-Based Active Learning for Node ClassificationCode0
Accelerating high-throughput virtual screening through molecular pool-based active learningCode1
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian ProcessesCode1
Semi-supervised Active Learning for Instance Segmentation via Scoring Predictions—0
Cost-Based Budget Active Learning for Deep Learning—0
A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design—0
Active Learning: Problem Settings and Recent Developments—0
Enhanced spatio-temporal electric load forecasts using less data with active deep learningCode1
Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval—0
Perfect density models cannot guarantee anomaly detection—0
What Makes a "Good" Data Augmentation in Knowledge Distillation -- A Statistical PerspectiveCode1
Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning—0
Bayesian Active Learning for Wearable Stress and Affect Detection—0
Stochastic Adversarial Gradient Embedding for Active Domain Adaptation—0
Sparse Semi-Supervised Action Recognition with Active Learning—0
Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning—0
Deep Multi-Fidelity Active Learning of High-dimensional Outputs—0
CORA: A Deep Active Learning Covid-19 Relevancy Algorithm to Identify Core Scientific Articles—0
Enhanced Labelling in Active Learning for Coreference Resolution—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