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 451500 of 3073 papers

TitleStatusHype
Active Learning with Safety Constraints0
Active emulation of computer codes with Gaussian processes -- Application to remote sensing0
Active Learning with Simple Questions0
Active Learning with Statistical Models0
Active Learning with Tabular Language Models0
Active Learning for Direct Preference Optimization0
Active Learning with TensorBoard Projector0
Active Learning with Transfer Learning0
Active Learning for Community Detection in Stochastic Block Models0
Active learning with version spaces for object detection0
Active Learning for Domain Classification in a Commercial Spoken Personal Assistant0
Active Learning with Point Supervision for Cost-Effective Panicle Detection in Cereal Crops0
Active Deep Learning on Entity Resolution by Risk Sampling0
ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios0
Active learning for efficient data selection in radio-signal based positioning via deep learning0
Actively learning a Bayesian matrix fusion model with deep side information0
Actively Learning Combinatorial Optimization Using a Membership Oracle0
Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies0
Active Learning for Efficient Testing of Student Programs0
Using Sum-Product Networks to Assess Uncertainty in Deep Active Learning0
Active learning for energy-based antibody optimization and enhanced screening0
Actively Learning Hemimetrics with Applications to Eliciting User Preferences0
Actively learning to learn causal relationships0
Actively Learning what makes a Discrete Sequence Valid0
Active Learning Polynomial Threshold Functions0
ActiveMatch: End-to-end Semi-supervised Active Representation Learning0
Active metric learning and classification using similarity queries0
Active Metric Learning for Supervised Classification0
Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment0
Active Mining Sample Pair Semantics for Image-text Matching0
Active Learning for Event Extraction with Memory-based Loss Prediction Model0
Active Model Aggregation via Stochastic Mirror Descent0
Active Learning for Fair and Stable Online Allocations0
Active Multi-Information Source Bayesian Quadrature0
Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification0
Active Multi-Task Representation Learning0
Active Nearest-Neighbor Learning in Metric Spaces0
Active deep learning method for the discovery of objects of interest in large spectroscopic surveys0
Active Neural 3D Reconstruction with Colorized Surface Voxel-based View Selection0
Active operator learning with predictive uncertainty quantification for partial differential equations0
Active Output Selection Strategies for Multiple Learning Regression Models0
Active partitioning: inverting the paradigm of active learning0
Active Perceptual Similarity Modeling with Auxiliary Information0
Active PETs: Active Data Annotation Prioritisation for Few-Shot Claim Verification with Pattern Exploiting Training0
Active Learning Over Multiple Domains in Natural Language Tasks0
Active Learning over DNN: Automated Engineering Design Optimization for Fluid Dynamics Based on Self-Simulated Dataset0
Active Preference Learning for Large Language Models0
Active Learning for Graph Neural Networks via Node Feature Propagation0
NE-LP: Normalized Entropy and Loss Prediction based Sampling for Active Learning in Chinese Word Segmentation on EHRs0
A Survey on Curriculum Learning0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified