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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.
Make Safe Decisions in Power System: Safe Reinforcement Learning Based Pre-decision Making for Voltage Stability Emergency Control
Active Learning for Finely-Categorized Image-Text Retrieval by Selecting Hard Negative Unpaired Samples
Actively Learning Combinatorial Optimization Using a Membership Oracle
One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models
Smooth Pseudo-Labeling
Lower Bound on the Greedy Approximation Ratio for Adaptive Submodular Cover
Amortized nonmyopic active search via deep imitation learning
Enhancing Active Learning for Sentinel 2 Imagery through Contrastive Learning and Uncertainty Estimation
What Makes Good Few-shot Examples for Vision-Language Models?
Active Object Detection with Knowledge Aggregation and Distillation from Large Models
An Active Learning Framework with a Class Balancing Strategy for Time Series Classification
A Unified Approach Towards Active Learning and Out-of-Distribution Detection
Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processes
ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios
Frugal Algorithm Selection
Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary Data
CloudS2Mask: A novel deep learning approach for improved cloud and cloud shadow masking in Sentinel-2 imagery
Perception Without Vision for Trajectory Prediction: Ego Vehicle Dynamics as Scene Representation for Efficient Active Learning in Autonomous Driving
Flexible image analysis for law enforcement agencies with deep neural networks to determine: where, who and what
Agnostic Active Learning of Single Index Models with Linear Sample Complexity
Neural Active Learning Meets the Partial Monitoring Framework
Maximizing Information Gain in Privacy-Aware Active Learning of Email Anomalies
Active Learning with Simple Questions
Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)
Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models
Active Preference Learning for Ordering Items In- and Out-of-sample
Active Neural 3D Reconstruction with Colorized Surface Voxel-based View Selection
Learning Linear Utility Functions From Pairwise Comparison Queries
Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition
Uncertainty for Active Learning on Graphs
Generative Active Learning for the Search of Small-molecule Protein Binders
Active Learning Enabled Low-cost Cell Image Segmentation Using Bounding Box Annotation
Active Learning with Task Adaptation Pre-training for Speech Emotion Recognition
A Survey on Deep Active Learning: Recent Advances and New Frontiers
Predictive Accuracy-Based Active Learning for Medical Image Segmentation
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