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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.
Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation
Active operator learning with predictive uncertainty quantification for partial differential equations
Active Learning for Deep Learning-Based Hemodynamic Parameter Estimation
CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance
Aggregation Strategies for Efficient Annotation of Bioacoustic Sound Events Using Active Learning
LAPD: Langevin-Assisted Bayesian Active Learning for Physical Discovery
Active Learning for Direct Preference Optimization
Architectural and Inferential Inductive Biases For Exchangeable Sequence Modeling
Comprehensive Evaluation of OCT-based Automated Segmentation of Retinal Layer, Fluid and Hyper-Reflective Foci: Impact on Diabetic Retinopathy Severity Assessment
DUAL: Diversity and Uncertainty Active Learning for Text Summarization
Bayesian Active Learning for Multi-Criteria Comparative Judgement in Educational Assessment
Rethinking Epistemic and Aleatoric Uncertainty for Active Open-Set Annotation: An Energy-Based Approach
Learning atomic forces from uncertainty-calibrated adversarial attacks
Distributionally Robust Active Learning for Gaussian Process Regression
Applying LLMs to Active Learning: Towards Cost-Efficient Cross-Task Text Classification without Manually Labeled Data
Active Learning for Conditional Inverse Design with Crystal Generation and Foundation Atomic Models
Active Learning Classification from a Signal Separation Perspective
AutoTandemML: Active Learning Enhanced Tandem Neural Networks for Inverse Design Problems
Generating $π$-Functional Molecules Using STGG+ with Active Learning
Physics-Informed Gaussian Process Classification for Constraint-Aware Alloy Design
From Selection to Generation: A Survey of LLM-based Active Learning
Enhancing RAG with Active Learning on Conversation Records: Reject Incapables and Answer Capables
Co-designing Large Language Model Tools for Project-Based Learning with K12 Educators
ActiveSSF: An Active-Learning-Guided Self-Supervised Framework for Long-Tailed Megakaryocyte Classification
Multifidelity Simulation-based Inference for Computationally Expensive Simulators
Towards a Foundation Model for Physics-Informed Neural Networks: Multi-PDE Learning with Active Sampling
Educating a Responsible AI Workforce: Piloting a Curricular Module on AI Policy in a Graduate Machine Learning Course
A physics-based data-driven model for CO$_2$ gas diffusion electrodes to drive automated laboratories
Probabilistic Artificial Intelligence
AL-PINN: Active Learning-Driven Physics-Informed Neural Networks for Efficient Sample Selection in Solving Partial Differential Equations
Automatic quantification of breast cancer biomarkers from multiple 18F-FDG PET image segmentation
DenseReviewer: A Screening Prioritisation Tool for Systematic Review based on Dense Retrieval
Mining Unstructured Medical Texts With Conformal Active Learning
Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics
Omni-Mol: Exploring Universal Convergent Space for Omni-Molecular Tasks
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