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 926–950 of 3073 papers

TitleStatusHype
Image Restoration with Point Spread Function Regularization and Active Learning—0
Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation—0
Model Uncertainty based Active Learning on Tabular Data using Boosted Trees—0
A Scalable Training Strategy for Blind Multi-Distribution Noise Removal—0
A Planning-and-Exploring Approach to Extreme-Mechanics Force Fields—0
A Competitive Algorithm for Agnostic Active Learning—0
Learning to Rank for Active Learning via Multi-Task Bilevel Optimization—0
MyriadAL: Active Few Shot Learning for HistopathologyCode0
Making RL with Preference-based Feedback Efficient via Randomization—0
Efficient Active Learning Halfspaces with Tsybakov Noise: A Non-convex Optimization Approach—0
Bayesian Active Learning in the Presence of Nuisance Parameters—0
Turn-Level Active Learning for Dialogue State TrackingCode0
ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation—0
MeaeQ: Mount Model Extraction Attacks with Efficient QueriesCode0
Cache & Distil: Optimising API Calls to Large Language Models—0
A Finite-Horizon Approach to Active Level Set Estimation—0
An active learning convolutional neural network for predicting river flow in a human impacted systemCode0
Pareto Optimization to Accelerate Multi-Objective Virtual Screening—0
Active Learning Framework for Cost-Effective TCR-Epitope Binding Affinity PredictionCode0
A Confidence-based Acquisition Model for Self-supervised Active Learning and Label Correction—0
Leveraging Optimal Transport for Enhanced Offline Reinforcement Learning in Surgical Robotic Environments—0
BaSAL: Size-Balanced Warm Start Active Learning for LiDAR Semantic SegmentationCode0
Aligning Data Selection with Performance: Performance-driven Reinforcement Learning for Active Learning in Object Detection—0
Constrained Bayesian Optimization with Adaptive Active Learning of Unknown Constraints—0
Refined Mechanism Design for Approximately Structured Priors via Active Regression—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