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

Showing 451–500 of 3073 papers

TitleStatusHype
Active Learning with Safety Constraints—0
Active emulation of computer codes with Gaussian processes -- Application to remote sensing—0
Active Learning with Simple Questions—0
Active Learning with Statistical Models—0
Active Learning with Tabular Language Models—0
Active Learning for Direct Preference Optimization—0
Active Learning with TensorBoard Projector—0
Active Learning with Transfer Learning—0
Active Learning for Community Detection in Stochastic Block Models—0
Active learning with version spaces for object detection—0
Active Learning for Domain Classification in a Commercial Spoken Personal Assistant—0
Active Learning with Point Supervision for Cost-Effective Panicle Detection in Cereal Crops—0
Active Deep Learning on Entity Resolution by Risk Sampling—0
ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios—0
Active learning for efficient data selection in radio-signal based positioning via deep learning—0
Actively learning a Bayesian matrix fusion model with deep side information—0
Actively Learning Combinatorial Optimization Using a Membership Oracle—0
Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies—0
Active Learning for Efficient Testing of Student Programs—0
Using Sum-Product Networks to Assess Uncertainty in Deep Active Learning—0
Active learning for energy-based antibody optimization and enhanced screening—0
Actively Learning Hemimetrics with Applications to Eliciting User Preferences—0
Actively learning to learn causal relationships—0
Actively Learning what makes a Discrete Sequence Valid—0
Active Learning Polynomial Threshold Functions—0
ActiveMatch: End-to-end Semi-supervised Active Representation Learning—0
Active metric learning and classification using similarity queries—0
Active Metric Learning for Supervised Classification—0
Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment—0
Active Mining Sample Pair Semantics for Image-text Matching—0
Active Learning for Event Extraction with Memory-based Loss Prediction Model—0
Active Model Aggregation via Stochastic Mirror Descent—0
Active Learning for Fair and Stable Online Allocations—0
Active Multi-Information Source Bayesian Quadrature—0
Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification—0
Active Multi-Task Representation Learning—0
Active Nearest-Neighbor Learning in Metric Spaces—0
Active deep learning method for the discovery of objects of interest in large spectroscopic surveys—0
Active Neural 3D Reconstruction with Colorized Surface Voxel-based View Selection—0
Active operator learning with predictive uncertainty quantification for partial differential equations—0
Active Output Selection Strategies for Multiple Learning Regression Models—0
Active partitioning: inverting the paradigm of active learning—0
Active Perceptual Similarity Modeling with Auxiliary Information—0
Active PETs: Active Data Annotation Prioritisation for Few-Shot Claim Verification with Pattern Exploiting Training—0
Active Learning Over Multiple Domains in Natural Language Tasks—0
Active Learning over DNN: Automated Engineering Design Optimization for Fluid Dynamics Based on Self-Simulated Dataset—0
Active Preference Learning for Large Language Models—0
Active Learning for Graph Neural Networks via Node Feature Propagation—0
NE-LP: Normalized Entropy and Loss Prediction based Sampling for Active Learning in Chinese Word Segmentation on EHRs—0
A Survey on Curriculum 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