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 1076–1100 of 3073 papers

TitleStatusHype
Re-Benchmarking Pool-Based Active Learning for Binary ClassificationCode0
Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local FilterCode0
A Proxy Attack-Free Strategy for Practically Improving the Poisoning Efficiency in Backdoor Attacks—0
Maestro: A Gamified Platform for Teaching AI Robustness—0
A Markovian Formalism for Active Querying—0
Active Learning Guided Fine-Tuning for enhancing Self-Supervised Based Multi-Label Classification of Remote Sensing Images—0
Adaptive Batch Sizes for Active Learning A Probabilistic Numerics ApproachCode0
Active-Learning-Driven Surrogate Modeling for Efficient Simulation of Parametric Nonlinear Systems—0
Actively learning a Bayesian matrix fusion model with deep side information—0
Training-Free Neural Active Learning with Initialization-Robustness GuaranteesCode0
Autonomous Capability Assessment of Sequential Decision-Making Systems in Stochastic Settings (Extended Version)Code0
NTKCPL: Active Learning on Top of Self-Supervised Model by Estimating True Coverage—0
A Dataset for Deep Learning-based Bone Structure Analyses in Total Hip ArthroplastyCode0
Active learning of the thermodynamics-dynamics tradeoff in protein condensates—0
How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget—0
Query Complexity of Active Learning for Function Family With Nearly Orthogonal Basis—0
Deep Active Learning with Structured Neural Depth Search—0
Advancing African-Accented Speech Recognition: Epistemic Uncertainty-Driven Data Selection for Generalizable ASR ModelsCode0
Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification—0
Active Learning on Medical Image—0
Agnostic Multi-Group Active Learning—0
CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions—0
Scaling Evidence-based Instructional Design Expertise through Large Language Models—0
Learning the Pareto Front Using Bootstrapped Observation Samples—0
atTRACTive: Semi-automatic white matter tract segmentation using active learningCode0
Show:102550
← PrevPage 44 of 123Next →

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