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 1801–1850 of 3073 papers

TitleStatusHype
Refined Mechanism Design for Approximately Structured Priors via Active Regression—0
regAL: Python Package for Active Learning of Regression Problems—0
ReGAL: Rule-Generative Active Learning for Model-in-the-Loop Weak Supervision—0
Region-level Active Detector Learning—0
Reinforced Meta Active Learning—0
Reinforcement-based Display-size Selection for Frugal Satellite Image Change Detection—0
Reinforcement-based frugal learning for satellite image change detection—0
Reinforcement Learning Approach to Active Learning for Image Classification—0
Reinforcement Learning from Human Feedback with Active Queries—0
Relevance feedback strategies for recall-oriented neural information retrieval—0
Reliability Estimation of an Advanced Nuclear Fuel using Coupled Active Learning, Multifidelity Modeling, and Subset Simulation—0
Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors—0
A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexity—0
Removing the Training Wheels: A Coreference Dataset that Entertains Humans and Challenges Computers—0
Rényi Entropy Bounds on the Active Learning Cost-Performance Tradeoff—0
Reserved Self-training: A Semi-supervised Sentiment Classification Method for Chinese Microblogs—0
Residual Gaussian Process: A Tractable Nonparametric Bayesian Emulator for Multi-fidelity Simulations—0
Resource Aware Multifidelity Active Learning for Efficient Optimization—0
Responsible Active Learning via Human-in-the-loop Peer Study—0
Restless Bandits with Many Arms: Beating the Central Limit Theorem—0
Rethinking Crowdsourcing Annotation: Partial Annotation with Salient Labels for Multi-Label Image Classification—0
Reversed Active Learning based Atrous DenseNet for Pathological Image Classification—0
Best Practices in Active Learning for Semantic Segmentation—0
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning—0
Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces—0
Revisiting Superpixels for Active Learning in Semantic Segmentation With Realistic Annotation Costs—0
Re-weighting Tokens: A Simple and Effective Active Learning Strategy for Named Entity Recognition—0
RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning—0
RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification—0
Robot Design With Neural Networks, MILP Solvers and Active Learning—0
Robust Active Distillation—0
Robust Active Learning for Electrocardiographic Signal Classification—0
Robust Active Learning (RoAL): Countering Dynamic Adversaries in Active Learning with Elastic Weight Consolidation—0
Robust Active Learning: Sample-Efficient Training of Robust Deep Learning Models—0
Robust Active Learning Strategies for Model Variability—0
Robust Adaptive Submodular Maximization—0
Robust and Active Learning for Deep Neural Network Regression—0
Robust and Discriminative Labeling for Multi-label Active Learning Based on Maximum Correntropy Criterion—0
Robust Assignment of Labels for Active Learning with Sparse and Noisy Annotations—0
Robust expected improvement for Bayesian optimization—0
Dimension-Robust MCMC in Bayesian Inverse Problems—0
Robustness of Bayesian Pool-based Active Learning Against Prior Misspecification—0
Robust online active learning—0
Robust Segmentation Models using an Uncertainty Slice Sampling Based Annotation Workflow—0
Robust Surgical Tools Detection in Endoscopic Videos with Noisy Data—0
Role-Playing Simulation Games using ChatGPT—0
RONAALP: Reduced-Order Nonlinear Approximation with Active Learning Procedure—0
RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian—0
S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification—0
SABAL: Sparse Approximation-based Batch Active Learning—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