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 351–400 of 3073 papers

TitleStatusHype
An active learning framework for multi-group mean estimation—0
Path-integral molecular dynamics with actively-trained and universal machine learning force fieldsCode0
Active Learning on Synthons for Molecular Design—0
Cell Library Characterization for Composite Current Source Models Based on Gaussian Process Regression and Active Learning—0
Designing and Contextualising Probes for African Languages—0
Community-based Multi-Agent Reinforcement Learning with Transfer and Active Exploration—0
Enhancing the Efficiency of Complex Systems Crystal Structure Prediction by Active Learning Guided Machine Learning Potential—0
Generalization Bounds and Stopping Rules for Learning with Self-Selected Data—0
Accelerating Battery Material Optimization through iterative Machine Learning—0
Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review—0
Constrained Online Decision-Making: A Unified Framework—0
Active Learning for Multi-class Image Classification—0
Exploring Multimodal Foundation AI and Expert-in-the-Loop for Sustainable Management of Wild Salmon Fisheries in Indigenous Rivers—0
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
Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering PerspectiveCode0
Label-efficient Single Photon Images Classification via Active Learning—0
Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise—0
RAFT: Robust Augmentation of FeaTures for Image Segmentation—0
AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active LearningCode0
The Search for Squawk: Agile Modeling in Bioacoustics—0
Reduced-order structure-property linkages for stochastic metamaterials—0
TActiLE: Tiny Active LEarning for wearable devices—0
Inconsistency-based Active Learning for LiDAR Object Detection—0
Subspace-Distance-Enabled Active Learning for Efficient Data-Driven Model Reduction of Parametric Dynamical Systems—0
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