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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.
Direct Acquisition Optimization for Low-Budget Active Learning
ActiveDP: Bridging Active Learning and Data Programming
An Artificial Intelligence (AI) workflow for catalyst design and optimization
Empowering Language Models with Active Inquiry for Deeper Understanding
Enhanced sampling of robust molecular datasets with uncertainty-based collective variables
ActiveAnno3D -- An Active Learning Framework for Multi-Modal 3D Object Detection
Information-Theoretic Active Correlation Clustering
Active Learning for Graphs with Noisy Structures
Foundation Model Makes Clustering A Better Initialization For Cold-Start Active Learning
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations
Deep Active Learning for Data Mining from Conflict Text Corpora
Automatic Segmentation of the Spinal Cord Nerve Rootlets
The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration
ActDroid: An active learning framework for Android malware detection
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image Segmentation
Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracy
SelectLLM: Can LLMs Select Important Instructions to Annotate?
A Study of Acquisition Functions for Medical Imaging Deep Active Learning
Graph-based Active Learning for Entity Cluster Repair
Revisiting Active Learning in the Era of Vision Foundation Models
Multitask Active Learning for Graph Anomaly Detection
Falcon: Fair Active Learning using Multi-armed Bandits
MORPH: Towards Automated Concept Drift Adaptation for Malware Detection
Learning from the Best: Active Learning for Wireless Communications
Navigating the Maize: Cyclic and conditional computational graphs for molecular simulation
Querying Easily Flip-flopped Samples for Deep Active Learning
Improving Classification Performance With Human Feedback: Label a few, we label the rest
Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active Learning
A Reproducibility Study of Goldilocks: Just-Right Tuning of BERT for TAR
Ship Detection in SAR Images with Human-in-the-Loop
ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation
Harnessing the Power of Beta Scoring in Deep Active Learning for Multi-Label Text Classification
Compute-Efficient Active Learning
Active Learning for NLP with Large Language Models
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