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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
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
CODA: A COst-efficient Test-time Domain Adaptation Mechanism for HAR
Generative Active Learning for Image Synthesis Personalization
DP-Dueling: Learning from Preference Feedback without Compromising User Privacy
Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks
Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Project
Annotation-Efficient Polyp Segmentation via Active Learning
A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science
Deep Active Learning: A Reality Check
Machine Learning Optimized Approach for Parameter Selection in MESHFREE Simulations
Uncertainty Driven Active Learning for Image Segmentation in Underwater Inspection
A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling
Boundary Matters: A Bi-Level Active Finetuning Framework
To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation
Deep Submodular Peripteral Networks
From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection and Active Learning
Physics-constrained Active Learning for Soil Moisture Estimation and Optimal Sensor Placement
Evolving Knowledge Distillation with Large Language Models and Active Learning
Active Generation for Image Classification
Domain Adversarial Active Learning for Domain Generalization Classification
Active Generalized Category Discovery
On the Topology Awareness and Generalization Performance of Graph Neural Networks
Generative Active Learning with Variational Autoencoder for Radiology Data Generation in Veterinary Medicine
Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training
SUPClust: Active Learning at the Boundaries
ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving
Active Statistical Inference
Active Learning of Mealy Machines with Timers
Improving Uncertainty Sampling with Bell Curve Weight Function
Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-trained Models
STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Models
Active Deep Kernel Learning of Molecular Functionalities: Realizing Dynamic Structural Embeddings
Improve Cost Efficiency of Active Learning over Noisy Dataset
Boosting Semi-Supervised Object Detection in Remote Sensing Images With Active Teaching
Accelerating materials discovery for polymer solar cells: Data-driven insights enabled by natural language processing
Automated Testing of Spatially-Dependent Environmental Hypotheses through Active Transfer Learning
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