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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 Structural-Clustering Based Active Learning for Graph Neural Networks
Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning
Transferable Candidate Proposal with Bounded Uncertainty
Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts
Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes
ActiveClean: Generating Line-Level Vulnerability Data via Active Learning
Few Clicks Suffice: Active Test-Time Adaptation for Semantic Segmentation
Federated Active Learning for Target Domain Generalisation
A Review and A Robust Framework of Data-Efficient 3D Scene Parsing with Traditional/Learned 3D Descriptors
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical Simulations
Benchmarking Multi-Domain Active Learning on Image Classification
Towards Comparable Active Learning
Active Foundational Models for Fault Diagnosis of Electrical Motors
FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models
The Battleship Approach to the Low Resource Entity Matching Problem
Leveraging deep active learning to identify low-resource mobility functioning information in public clinical notes
Comprehensive Benchmarking of Entropy and Margin Based Scoring Metrics for Data Selection
One-bit Supervision for Image Classification: Problem, Solution, and Beyond
A unified framework for learning with nonlinear model classes from arbitrary linear samples
Relevance feedback strategies for recall-oriented neural information retrieval
Testable Learning with Distribution Shift
Class Balanced Dynamic Acquisition for Domain Adaptive Semantic Segmentation using Active Learning
Multi-Objective Bayesian Optimization with Active Preference Learning
Evidential Uncertainty Quantification: A Variance-Based Perspective
Active Prompt Learning in Vision Language Models
RONAALP: Reduced-Order Nonlinear Approximation with Active Learning Procedure
FOCAL: A Cost-Aware Video Dataset for Active Learning
Human Still Wins over LLM: An Empirical Study of Active Learning on Domain-Specific Annotation Tasks
Correlation-aware active learning for surgery video segmentation
Batch Selection and Communication for Active Learning with Edge Labeling
Generation Of Colors using Bidirectional Long Short Term Memory Networks
Ulcerative Colitis Mayo Endoscopic Scoring Classification with Active Learning and Generative Data Augmentation
DeMuX: Data-efficient Multilingual Learning
Data Distillation for Neural Network Potentials toward Foundational Dataset
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