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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.
Reducing Aleatoric and Epistemic Uncertainty through Multi-modal Data Acquisition
Breaking the SSL-AL Barrier: A Synergistic Semi-Supervised Active Learning Framework for 3D Object Detection
Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies from Simulated Nonparametric Functions
Active Learning for Continual Learning: Keeping the Past Alive in the Present
Gaussian-Process-based Adaptive Tracking Control with Dynamic Active Learning for Autonomous Ground Vehicles
Hybrid Interpretable Deep Learning Framework for Skin Cancer Diagnosis: Integrating Radial Basis Function Networks with Explainable AI
Efficient Auto-Labeling of Large-Scale Poultry Datasets (ALPD) Using Semi-Supervised Models, Active Learning, and Prompt-then-Detect Approach
Big Batch Bayesian Active Learning by Considering Predictive Probabilities
Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context Adaptation
Mechanics and Design of Metastructured Auxetic Patches with Bio-inspired Materials
Advanced Tutorial: Label-Efficient Two-Sample Tests
Active Learning Enables Extrapolation in Molecular Generative Models
Bayesian Active Learning By Distribution Disagreement
U-GIFT: Uncertainty-Guided Firewall for Toxic Speech in Few-Shot Scenario
Efficient Human-in-the-Loop Active Learning: A Novel Framework for Data Labeling in AI Systems
Active Learning of General Halfspaces: Label Queries vs Membership Queries
Uncertainty Herding: One Active Learning Method for All Label Budgets
Active Learning with Variational Quantum Circuits for Quantum Process Tomography
STAYKATE: Hybrid In-Context Example Selection Combining Representativeness Sampling and Retrieval-based Approach -- A Case Study on Science Domains
Image Classification with Deep Reinforcement Active Learning
TSceneJAL: Joint Active Learning of Traffic Scenes for 3D Object Detection
Uncertainty Quantification in Continual Open-World Learning
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles
GALOT: Generative Active Learning via Optimizable Zero-shot Text-to-image Generation
Active Reinforcement Learning Strategies for Offline Policy Improvement
AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific Discovery
ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized Experiments
From Easy to Hard: Progressive Active Learning Framework for Infrared Small Target Detection with Single Point Supervision
Active Large Language Model-based Knowledge Distillation for Session-based Recommendation
An Active Parameter Learning Approach to The Identification of Safe Regions
Safe Active Learning for Gaussian Differential Equations
Enhancing Modality Representation and Alignment for Multimodal Cold-start Active Learning
The Cost of Replicability in Active Learning
Congruence-based Learning of Probabilistic Deterministic Finite Automata
How to select slices for annotation to train best-performing deep learning segmentation models for cross-sectional medical images?
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