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 51–100 of 3073 papers

TitleStatusHype
GFlowNets for Active Learning Based Resource Allocation in Next Generation Wireless Networks—0
Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable ModelsCode0
RAFT: Robust Augmentation of FeaTures for Image Segmentation—0
Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise—0
Label-efficient Single Photon Images Classification via Active Learning—0
AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active LearningCode0
The Search for Squawk: Agile Modeling in Bioacoustics—0
Sailing AI by the Stars: A Survey of Learning from Rewards in Post-Training and Test-Time Scaling of Large Language ModelsCode2
TActiLE: Tiny Active LEarning for wearable devices—0
Reduced-order structure-property linkages for stochastic metamaterials—0
Inconsistency-based Active Learning for LiDAR Object Detection—0
HeAL3D: Heuristical-enhanced Active Learning for 3D Object Detection—0
Subspace-Distance-Enabled Active Learning for Efficient Data-Driven Model Reduction of Parametric Dynamical Systems—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
Efficient Process Reward Model Training via Active LearningCode1
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
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
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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