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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.
Improving Interpretability of Deep Active Learning for Flood Inundation Mapping Through Class Ambiguity Indices Using Multi-spectral Satellite Imagery
Making Better Use of Unlabelled Data in Bayesian Active Learning
Annotator-Centric Active Learning for Subjective NLP Tasks
Generative Subspace Adversarial Active Learning for Outlier Detection in Multiple Views of High-dimensional Data
Language-Driven Active Learning for Diverse Open-Set 3D Object Detection
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning
Physics-informed active learning for accelerating quantum chemical simulations
Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory
Neural Active Learning Beyond Bandits
Classification Tree-based Active Learning: A Wrapper Approach
Epistemic Uncertainty Quantification For Pre-trained Neural Network
Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image Classification
Active Learning for Control-Oriented Identification of Nonlinear Systems
Experimental Design for Active Transductive Inference in Large Language Models
Interactive Ontology Matching with Cost-Efficient Learning
SQBC: Active Learning using LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions
AI-Guided Defect Detection Techniques to Model Single Crystal Diamond Growth
AI-Guided Feature Segmentation Techniques to Model Features from Single Crystal Diamond Growth
AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets
Active Test-Time Adaptation: Theoretical Analyses and An Algorithm
Focused Active Learning for Histopathological Image Classification
ProtoAL: Interpretable Deep Active Learning with prototypes for medical imaging
Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions
Conversational Disease Diagnosis via External Planner-Controlled Large Language Models
Active learning for efficient annotation in precision agriculture: a use-case on crop-weed semantic segmentation
Hallucination Diversity-Aware Active Learning for Text Summarization
Uncertainty-aware Active Learning of NeRF-based Object Models for Robot Manipulators using Visual and Re-orientation Actions
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation
Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies
LLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource Languages
Using Chao's Estimator as a Stopping Criterion for Technology-Assisted Review
Collaborative Active Learning in Conditional Trust Environment
Few-shot Named Entity Recognition via Superposition Concept Discrimination
Active Learning of Dynamics Using Prior Domain Knowledge in the Sampling Process
An active learning model to classify animal species in Hong Kong
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