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 726–750 of 3073 papers

TitleStatusHype
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models—0
ALEX: Active Learning based Enhancement of a Model's Explainability—0
An Eye-tracking Study of Named Entity Annotation—0
An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting—0
An information-matching approach to optimal experimental design and active learning—0
Active Learning of Convex Halfspaces on Graphs—0
Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-based Multiple Instance Learning—0
Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions—0
An Intelligent Extraversion Analysis Scheme from Crowd Trajectories for Surveillance—0
ALEVS: Active Learning by Statistical Leverage Sampling—0
Annotating named entities in clinical text by combining pre-annotation and active learning—0
Annotating Social Determinants of Health Using Active Learning, and Characterizing Determinants Using Neural Event Extraction—0
Annotation Cost Efficient Active Learning for Content Based Image Retrieval—0
Annotation Cost-Efficient Active Learning for Deep Metric Learning Driven Remote Sensing Image Retrieval—0
Annotation Efficiency: Identifying Hard Samples via Blocked Sparse Linear Bandits—0
Annotation-Efficient Polyp Segmentation via Active Learning—0
Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation—0
Active Learning of General Halfspaces: Label Queries vs Membership Queries—0
Anomaly Detection in Hierarchical Data Streams under Unknown Models—0
Anomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning—0
Active Learning for Accurate Estimation of Linear Models—0
An optimal transport approach for selecting a representative subsample with application in efficient kernel density estimation—0
A novel active learning-based Gaussian process metamodelling strategy for estimating the full probability distribution in forward UQ analysis—0
Active Learning for Vision-Language Models—0
Accurate Prediction and Uncertainty Estimation using Decoupled Prediction Interval Networks—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