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 76–100 of 3073 papers

TitleStatusHype
Towards Unconstrained 2D Pose Estimation of the Human Spine—0
The Work Capacity of Channels with Memory: Maximum Extractable Work in Percept-Action Loops—0
Low Rank Learning for Offline Query OptimizationCode0
Optimal Bayesian Affine Estimator and Active Learning for the Wiener ModelCode0
Diffusion Active Learning: Towards Data-Driven Experimental Design in Computed Tomography—0
FAST: Federated Active Learning with Foundation Models for Communication-efficient Sampling and Training—0
Hide and Seek in Noise Labels: Noise-Robust Collaborative Active Learning with LLM-Powered Assistance—0
Anomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning—0
CoTAL: Human-in-the-Loop Prompt Engineering, Chain-of-Thought Reasoning, and Active Learning for Generalizable Formative Assessment Scoring—0
Horizon Scans can be accelerated using novel information retrieval and artificial intelligence tools—0
Active Learning Design: Modeling Force Output for Axisymmetric Soft Pneumatic ActuatorsCode0
Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHF—0
Confidence Adjusted Surprise Measure for Active Resourceful Trials (CA-SMART): A Data-driven Active Learning Framework for Accelerating Material Discovery under Resource Constraints—0
Fairness-Driven LLM-based Causal Discovery with Active Learning and Dynamic Scoring—0
Audio-Enhanced Vision-Language Modeling with Latent Space Broadening for High Quality Data Expansion—0
Active Learning For Repairable Hardware Systems With Partial Coverage—0
Uncertainty Meets Diversity: A Comprehensive Active Learning Framework for Indoor 3D Object Detection—0
Efficient Data Selection for Training Genomic Perturbation Models—0
Active Learning from Scene Embeddings for End-to-End Autonomous Driving—0
Preference Elicitation for Multi-objective Combinatorial Optimization with Active Learning and Maximum Likelihood Estimation—0
Have LLMs Made Active Learning Obsolete? Surveying the NLP Community—0
Active Learning Inspired ControlNet Guidance for Augmenting Semantic Segmentation Datasets—0
Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and SolutionsCode0
QuickDraw: Fast Visualization, Analysis and Active Learning for Medical Image SegmentationCode0
Generative method for aerodynamic optimization based on classifier-free guided denoising diffusion probabilistic model—0
Show:102550
← PrevPage 4 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