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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.
Interactive Event Sifting using Bayesian Graph Neural Networks
Language Model-Driven Data Pruning Enables Efficient Active Learning
STONE: A Submodular Optimization Framework for Active 3D Object Detection
Active Learning of Deep Neural Networks via Gradient-Free Cutting Planes
Structural-Entropy-Based Sample Selection for Efficient and Effective Learning
Dual Active Learning for Reinforcement Learning from Human Feedback
GPT-4o as the Gold Standard: A Scalable and General Purpose Approach to Filter Language Model Pretraining Data
Provably Accurate Shapley Value Estimation via Leverage Score Sampling
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
Efficient Quality Control of Whole Slide Pathology Images with Human-in-the-loop Training
Sustaining model performance for covid-19 detection from dynamic audio data: Development and evaluation of a comprehensive drift-adaptive framework
A3: Active Adversarial Alignment for Source-Free Domain Adaptation
AL-GTD: Deep Active Learning for Gaze Target Detection
Towards an active-learning approach to resource allocation for population-based damage prognosis
Dirichlet-Based Coarse-to-Fine Example Selection For Open-Set Annotation
Find Rhinos without Finding Rhinos: Active Learning with Multimodal Imagery of South African Rhino Habitats
Reactive Multi-Robot Navigation in Outdoor Environments Through Uncertainty-Aware Active Learning of Human Preference Landscape
Open-/Closed-loop Active Learning for Data-driven Predictive Control
From Passive Watching to Active Learning: Empowering Proactive Participation in Digital Classrooms with AI Video Assistant
CAMAL: Optimizing LSM-trees via Active Learning
Critic Loss for Image Classification
The trade-off between data minimization and fairness in collaborative filtering
Enhancing Semi-Supervised Learning via Representative and Diverse Sample Selection
Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning
SANE: Strategic Autonomous Non-Smooth Exploration for Multiple Optima Discovery in Multi-modal and Non-differentiable Black-box Functions
Active learning for energy-based antibody optimization and enhanced screening
Diversify and Conquer: Diversity-Centric Data Selection with Iterative Refinement
Active Learning to Guide Labeling Efforts for Question Difficulty Estimation
MALADY: Multiclass Active Learning with Auction Dynamics on Graphs
DEMAU: Decompose, Explore, Model and Analyse Uncertainties
Crown-Like Structures in Breast Adipose Tissue: Finding a 'Needle-in-a-Haystack' using Artificial Intelligence and Collaborative Active Learning on the Web
FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression
A Scalable Algorithm for Active Learning
Automated Discovery of Pairwise Interactions from Unstructured Data
STAND: Data-Efficient and Self-Aware Precondition Induction for Interactive Task Learning
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