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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.
ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data
The Use of AI-Robotic Systems for Scientific Discovery
Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial Optimization
Generative AI for Enhancing Active Learning in Education: A Comparative Study of GPT-3.5 and GPT-4 in Crafting Customized Test Questions
Active Learning for Fair and Stable Online Allocations
Large-Scale Dataset Pruning in Adversarial Training through Data Importance Extrapolation
Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center Dataset
Towards Bayesian Data Selection
Active search for Bifurcations
Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classification
Enhancing Text Classification through LLM-Driven Active Learning and Human Annotation
SS-ADA: A Semi-Supervised Active Domain Adaptation Framework for Semantic Segmentation
Deep Bayesian Active Learning for Preference Modeling in Large Language Models
Annotation Cost-Efficient Active Learning for Deep Metric Learning Driven Remote Sensing Image Retrieval
Understanding active learning of molecular docking and its applications
Self-Training for Sample-Efficient Active Learning for Text Classification with Pre-Trained Language Models
Parameter-Efficient Active Learning for Foundational models
Online Bandit Learning with Offline Preference Data for Improved RLHF
EFFOcc: A Minimal Baseline for EFficient Fusion-based 3D Occupancy Network
Active learning for affinity prediction of antibodies
Quantifying Local Model Validity using Active Learning
Greedy SLIM: A SLIM-Based Approach For Preference Elicitation
Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples
Simulating, Fast and Slow: Learning Policies for Black-Box Optimization
"Give Me an Example Like This": Episodic Active Reinforcement Learning from Demonstrations
Active ML for 6G: Towards Efficient Data Generation, Acquisition, and Annotation
Generative Active Learning for Long-tailed Instance Segmentation
Effective Data Selection for Seismic Interpretation through Disagreement
Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model Selection
Towards Efficient Disaster Response via Cost-effective Unbiased Class Rate Estimation through Neyman Allocation Stratified Sampling Active Learning
A Survey of Latent Factor Models in Recommender Systems
Edge-guided and Class-balanced Active Learning for Semantic Segmentation of Aerial Images
A Data-Centric Framework for Machine Listening Projects: Addressing Large-Scale Data Acquisition and Labeling through Active Learning
Entity Alignment with Noisy Annotations from Large Language Models
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