Papers with Code 아카이브
Active Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 1
- 보존된 평가 기록
- 7
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.
A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences
Near-Polynomially Competitive Active Logistic Regression
A Risk-Aware Adaptive Robust MPC with Learned Uncertainty Quantification
MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials
CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization
Active Learning for Manifold Gaussian Process Regression
Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization
Active Learning-Guided Seq2Seq Variational Autoencoder for Multi-target Inhibitor Generation
Bayesian Active Learning of (small) Quantile Sets through Expected Estimator Modification
Coupled reaction and diffusion governing interface evolution in solid-state batteries
GRAIL: A Benchmark for GRaph ActIve Learning in Dynamic Sensing Environments
Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
Info-Coevolution: An Efficient Framework for Data Model Coevolution
Domain Switching on the Pareto Front: Multi-Objective Deep Kernel Learning in Automated Piezoresponse Force Microscopy
ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition
Active Test-time Vision-Language Navigation
Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system
An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron Diffractometry
ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation
Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Grid
NepTrain and NepTrainKit: Automated Active Learning and Visualization Toolkit for Neuroevolution Potentials
Active Learning via Regression Beyond Realizability
Extending AALpy with Passive Learning: A Generalized State-Merging Approach
Bayesian Neural Scaling Laws Extrapolation with Prior-Fitted Networks
Aurora: Are Android Malware Classifiers Reliable and Stable under Distribution Shift?
MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models
Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event Detection
Active Learning-Enhanced Dual Control for Angle-Only Initial Relative Orbit Determination
Exploring the Possibility of TypiClust for Low-Budget Federated Active Learning
Language Model-Enhanced Message Passing for Heterophilic Graph Learning
Efficient Deconvolution in Populational Inverse Problems
Monocle: Hybrid Local-Global In-Context Evaluation for Long-Text Generation with Uncertainty-Based Active Learning
LLM-Guided Taxonomy and Hierarchical Uncertainty for 3D Point CLoud Active Learning
Alignment and Safety of Diffusion Models via Reinforcement Learning and Reward Modeling: A Survey
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.