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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.
Dirichlet Active Learning
Optimal simulation-based Bayesian decisions
Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble based sample selection
Active Transfer Learning for Efficient Video-Specific Human Pose Estimation
Learning to Learn for Few-shot Continual Active Learning
Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling
Perturbation-based Active Learning for Question Answering
Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural Networks
Active Learning-Based Species Range Estimation
Re-weighting Tokens: A Simple and Effective Active Learning Strategy for Named Entity Recognition
Incentivized Collaboration in Active Learning
Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation
Image Restoration with Point Spread Function Regularization and Active Learning
A Planning-and-Exploring Approach to Extreme-Mechanics Force Fields
Model Uncertainty based Active Learning on Tabular Data using Boosted Trees
LLMaAA: Making Large Language Models as Active Annotators
Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selection
A Scalable Training Strategy for Blind Multi-Distribution Noise Removal
Learning to Rank for Active Learning via Multi-Task Bilevel Optimization
MyriadAL: Active Few Shot Learning for Histopathology
Turn-Level Active Learning for Dialogue State Tracking
Making RL with Preference-based Feedback Efficient via Randomization
Bayesian Active Learning in the Presence of Nuisance Parameters
ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation
Efficient Active Learning Halfspaces with Tsybakov Noise: A Non-convex Optimization Approach
A comprehensive survey on deep active learning in medical image analysis
MeaeQ: Mount Model Extraction Attacks with Efficient Queries
Cache & Distil: Optimising API Calls to Large Language Models
A Finite-Horizon Approach to Active Level Set Estimation
An active learning convolutional neural network for predicting river flow in a human impacted system
Open-CRB: Towards Open World Active Learning for 3D Object Detection
Pareto Optimization to Accelerate Multi-Objective Virtual Screening
Active Learning Framework for Cost-Effective TCR-Epitope Binding Affinity Prediction
Leveraging Optimal Transport for Enhanced Offline Reinforcement Learning in Surgical Robotic Environments
A Confidence-based Acquisition Model for Self-supervised Active Learning and Label Correction
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