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Active Learning

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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

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Henrium/ET-AL

공식 코드

ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data

https://github.com/IDEALLab/Active-Expansion-Sampling

공식 코드

Active Expansion Sampling for Learning Feasible Domains in an Unbounded Input Space

https://github.com/IuryBAS/ProtoAL_paper

공식 코드

ProtoAL: Interpretable Deep Active Learning with prototypes for medical imaging

https://github.com/JialongJiang/Active_learning_spin

공식 코드

Active Learning of Spin Network Models

https://github.com/JohnLangford/vowpal_wabbit

공식 코드

On the reusability of samples in active learning

https://github.com/JonathanWenger/pycalib

공식 코드

Non-Parametric Calibration for Classification

https://github.com/JordanAsh/badge

공식 코드

Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds

https://github.com/Kaleidophon/deep-significance

공식 코드

Cartography Active Learning

https://github.com/KirillShmilovich/ActiveLearningCG

공식 코드

Discovery of Self-Assembling $π$-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation

https://github.com/LCSL/incremental_multiclass_RLSC

공식 코드

Incremental Robot Learning of New Objects with Fixed Update Time

https://github.com/MH-Hou/EARLY

공식 코드

"Give Me an Example Like This": Episodic Active Reinforcement Learning from Demonstrations

https://github.com/MJ10/DEUP

공식 코드

DEUP: Direct Epistemic Uncertainty Prediction

https://github.com/MMunibas/PhysNet_DER

공식 코드

Uncertainty quantification for predictions of atomistic neural networks

https://github.com/Mars-tin/GraphPart

공식 코드

Partition-Based Active Learning for Graph Neural Networks

https://github.com/MarsalekGroup/paper-c-nnp

공식 코드

Committee neural network potentials control generalization errors and enable active learning

https://github.com/MengShen0709/bmmal

공식 코드

Towards Balanced Active Learning for Multimodal Classification

https://github.com/MiriamHu/ActiveBoundary

공식 코드

Active Decision Boundary Annotation with Deep Generative Models

https://github.com/MrGiovanni/Active-Learning

공식 코드

Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts

https://github.com/NTU-Siqiang-Group/CAMAL

공식 코드

CAMAL: Optimizing LSM-trees via Active Learning

https://github.com/NUAA-AL/ALiPy

공식 코드

ALiPy: Active Learning in Python

https://github.com/Naagar/Seeds_Classification

공식 코드

Automated Seed Quality Testing System using GAN & Active Learning

https://github.com/NeuralSentinel/DistALANER

공식 코드

DistALANER: Distantly Supervised Active Learning Augmented Named Entity Recognition in the Open Source Software Ecosystem

https://github.com/NoelShin/PixelPick

공식 코드

All you need are a few pixels: semantic segmentation with PixelPick

https://github.com/OmarSababha/active_ML

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Active ML for 6G: Towards Efficient Data Generation, Acquisition, and Annotation

https://github.com/PV-Lab/Benchmarking

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Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains

https://github.com/Pearl-UTexas/ActiveVaR

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Risk-Aware Active Inverse Reinforcement Learning

https://github.com/PengyiZhang/MIADeepSSL

공식 코드

A Survey on Deep Learning of Small Sample in Biomedical Image Analysis

https://github.com/RelationRx/pyrelational

공식 코드

PyRelationAL: a python library for active learning research and development

https://github.com/RishiHazra/Actively-reducing-redundancies-in-Active-Learning-for-Sequence-Tagging

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Active$^2$ Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation

https://github.com/RookieRQ/AL-enhanced-Surrogate_MLMC-Adequacy_Assessment

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MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models

https://github.com/SPEECHCOG/cpc_pytorch

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Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion Recognition

https://github.com/SUNCAT-Center/CatLearn

공식 코드

An Atomistic Machine Learning Package for Surface Science and Catalysis

https://github.com/SamanthaBiegel/ActiveWeaSuL

공식 코드

Active WeaSuL: Improving Weak Supervision with Active Learning

https://github.com/SciEcon/Active_Learning

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The Right Tool for the Job: Matching Active Learning Techniques to Learning Objectives

https://github.com/Seondong/Customs-Fraud-Detection

공식 코드

Active Learning for Human-in-the-Loop Customs Inspection

https://github.com/Shen-Lab/BAL

공식 코드

Bayesian active learning for optimization and uncertainty quantification in protein docking

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