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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.
Deep Active Learning in the Open World
FisherMask: Enhancing Neural Network Labeling Efficiency in Image Classification Using Fisher Information
GCI-ViTAL: Gradual Confidence Improvement with Vision Transformers for Active Learning on Label Noise
UEVAVD: A Dataset for Developing UAV's Eye View Active Object Detection
Hands-On Tutorial: Labeling with LLM and Human-in-the-Loop
Performance-Guided LLM Knowledge Distillation for Efficient Text Classification at Scale
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation
An information-matching approach to optimal experimental design and active learning
Exploiting Contextual Uncertainty of Visual Data for Efficient Training of Deep Models
Machine Learning-Accelerated Multi-Objective Design of Fractured Geothermal Systems
Cost-Aware Query Policies in Active Learning for Efficient Autonomous Robotic Exploration
SpiroActive: Active Learning for Efficient Data Acquisition for Spirometry
Active Learning for Vision-Language Models
DISCERN: Decoding Systematic Errors in Natural Language for Text Classifiers
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials
Annotation Efficiency: Identifying Hard Samples via Blocked Sparse Linear Bandits
Efficient Biological Data Acquisition through Inference Set Design
Uncertainty-Error correlations in Evidential Deep Learning models for biomedical segmentation
Exploring the Universe with SNAD: Anomaly Detection in Astronomy
Perturbation-based Graph Active Learning for Weakly-Supervised Belief Representation Learning
regAL: Python Package for Active Learning of Regression Problems
Bayesian optimization for robust robotic grasping using a sensorized compliant hand
Deep Active Learning with Manifold-preserving Trajectory Sampling
Learning signals defined on graphs with optimal transport and Gaussian process regression
Increasing Interpretability of Neural Networks By Approximating Human Visual Saliency
Coherence-Driven Multimodal Safety Dialogue with Active Learning for Embodied Agents
A Simplifying and Learnable Graph Convolutional Attention Network for Unsupervised Knowledge Graphs Alignment
Railway LiDAR semantic segmentation based on intelligent semi-automated data annotation
An Active Learning Framework for Inclusive Generation by Large Language Models
AutoAL: Automated Active Learning with Differentiable Query Strategy Search
MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning
Active Learning for Robust and Representative LLM Generation in Safety-Critical Scenarios
ALVIN: Active Learning Via INterpolation
Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs
MelissaDL x Breed: Towards Data-Efficient On-line Supervised Training of Multi-parametric Surrogates with Active Learning
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