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 376–400 of 3073 papers

TitleStatusHype
HeAL3D: Heuristical-enhanced Active Learning for 3D Object Detection—0
Curiosity Driven Exploration to Optimize Structure-Property Learning in MicroscopyCode0
Context Selection and Rewriting for Video-based Educational Question GenerationCode0
Geometry-aware Active Learning of Spatiotemporal Dynamic Systems—0
Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security ApplicationsCode0
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated Active Learning—0
Compositional Active Learning of Synchronizing Systems through Automated Alphabet Refinement—0
From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System—0
Simulating Before Planning: Constructing Intrinsic User World Model for User-Tailored Dialogue Policy Planning—0
Parsimonious Dataset Construction for Laparoscopic Cholecystectomy Structure Segmentation—0
Uncertainty Quantification in Graph Neural Networks with Shallow Ensembles—0
Scholar Inbox: Personalized Paper Recommendations for Scientists—0
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
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
Hide and Seek in Noise Labels: Noise-Robust Collaborative Active Learning with LLM-Powered Assistance—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
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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