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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.
MAPLE: A Framework for Active Preference Learning Guided by Large Language Models
Label Distribution Learning using the Squared Neural Family on the Probability Simplex
Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data
Enhanced Multi-Object Tracking Using Pose-based Virtual Markers in 3x3 Basketball
Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation
Class Balance Matters to Active Class-Incremental Learning
Materials-Discovery Workflows Guided by Symbolic Regression: Identifying Acid-Stable Oxides for Electrocatalysis
Post-hoc Probabilistic Vision-Language Models
Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference
Multi-Layer Privacy-Preserving Record Linkage with Clerical Review based on gradual information disclosure
Learning-by-teaching with ChatGPT: The effect of teachable ChatGPT agent on programming education
Superposition through Active Learning lens
Active Learning via Classifier Impact and Greedy Selection for Interactive Image Retrieval
Sample Efficient Robot Learning in Supervised Effect Prediction Tasks
Active learning of neural population dynamics using two-photon holographic optogenetics
Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition
PAL -- Parallel active learning for machine-learned potentials
Neural Window Decoder for SC-LDPC Codes
ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System
Spectral-Spatial Transformer with Active Transfer Learning for Hyperspectral Image Classification
Active partitioning: inverting the paradigm of active learning
Maximally Separated Active Learning
Multi-Label Bayesian Active Learning with Inter-Label Relationships
A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation
Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation
Benchmarking Active Learning for NILM
Influence functions and regularity tangents for efficient active learning
Active Learning-Based Optimization of Hydroelectric Turbine Startup to Minimize Fatigue Damage
Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties
LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training
Stream-Based Active Learning for Process Monitoring
Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimation
Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space
MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild
Targeting Negative Flips in Active Learning using Validation Sets
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