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Active Learning
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
A Simple Approximation Algorithm for Optimal Decision Tree
Path-integral molecular dynamics with actively-trained and universal machine learning force fields
An active learning framework for multi-group mean estimation
Active Learning on Synthons for Molecular Design
Cell Library Characterization for Composite Current Source Models Based on Gaussian Process Regression and Active Learning
Designing and Contextualising Probes for African Languages
InvDesFlow-AL: Active Learning-based Workflow for Inverse Design of Functional Materials
Community-based Multi-Agent Reinforcement Learning with Transfer and Active Exploration
Enhancing the Efficiency of Complex Systems Crystal Structure Prediction by Active Learning Guided Machine Learning Potential
Generalization Bounds and Stopping Rules for Learning with Self-Selected Data
Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review
Accelerating Battery Material Optimization through iterative Machine Learning
Active Learning for Multi-class Image Classification
Constrained Online Decision-Making: A Unified Framework
Exploring Multimodal Foundation AI and Expert-in-the-Loop for Sustainable Management of Wild Salmon Fisheries in Indigenous Rivers
Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models
GFlowNets for Active Learning Based Resource Allocation in Next Generation Wireless Networks
Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective
Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise
Label-efficient Single Photon Images Classification via Active Learning
RAFT: Robust Augmentation of FeaTures for Image Segmentation
AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning
Sailing AI by the Stars: A Survey of Learning from Rewards in Post-Training and Test-Time Scaling of Large Language Models
The Search for Squawk: Agile Modeling in Bioacoustics
TActiLE: Tiny Active LEarning for wearable devices
Reduced-order structure-property linkages for stochastic metamaterials
Subspace-Distance-Enabled Active Learning for Efficient Data-Driven Model Reduction of Parametric Dynamical Systems
HeAL3D: Heuristical-enhanced Active Learning for 3D Object Detection
Inconsistency-based Active Learning for LiDAR Object Detection
Context Selection and Rewriting for Video-based Educational Question Generation
Curiosity Driven Exploration to Optimize Structure-Property Learning in Microscopy
Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security Applications
Geometry-aware Active Learning of Spatiotemporal Dynamic Systems
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated Active Learning
Compositional Active Learning of Synchronizing Systems through Automated Alphabet Refinement
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