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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/kalra20/RISAN-Robust-Instance-Specific-Abstain-Network

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RISAN: Robust Instance Specific Abstention Network

https://github.com/kerembozgann/adaptive-epsilon-pal

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Beyond Grids: Multi-objective Bayesian Optimization With Adaptive Discretization

https://github.com/kerrydrx/evidentialada

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Evidential Uncertainty Quantification: A Variance-Based Perspective

https://github.com/khtae8250/SliceTuner

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Slice Tuner: A Selective Data Acquisition Framework for Accurate and Fair Machine Learning Models

https://github.com/khtae8250/falcon

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Falcon: Fair Active Learning using Multi-armed Bandits

https://github.com/kjetil-lye/ismo_airfoil

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Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks

https://github.com/kjetil-lye/ismo_heat

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Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks

https://github.com/kjetil-lye/ismo_validation

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Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks

https://github.com/kjetil-lye/iterative_surrogate_optimization

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Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks

https://github.com/kkalais/stochlwta-ml

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Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning

https://github.com/kli58/ROIAL

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ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes

https://github.com/kmalialis/actisiamese

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Nonstationary data stream classification with online active learning and siamese neural networks

https://github.com/kmalialis/augmented_queues

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Data augmentation on-the-fly and active learning in data stream classification

https://github.com/koushik-n/Active-Learning-Plant-Phenotyping

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How useful is Active Learning for Image-based Plant Phenotyping?

https://github.com/kritisingh24/active_learning_nmt

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Active Learning for Neural Machine Translation

https://github.com/kschwethelm/dpal

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Differentially Private Active Learning: Balancing Effective Data Selection and Privacy

https://github.com/ksenia-konyushkova/LAL

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Learning Active Learning from Data

https://github.com/ksenia-konyushkova/LAL-RL

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Discovering General-Purpose Active Learning Strategies

https://github.com/kunalghosh/multi_fidelity_prediction_gp

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Active Learning of Molecular Data for Task-Specific Objectives

https://github.com/lackel/loop

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Generalized Category Discovery with Large Language Models in the Loop

https://github.com/lai1997/bmlp_active_public

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Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models

https://github.com/leaplabthu/activenerf

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ActiveNeRF: Learning where to See with Uncertainty Estimation

https://github.com/learningmatter-mit/Atomistic-Adversarial-Attacks

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Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks

https://github.com/learningmatter-mit/surface-sampling

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Machine-learning-accelerated simulations to enable automatic surface reconstruction

https://github.com/learningmatter-mit/uq_singlenn

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Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles

https://github.com/lee-cbg/activetcr

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Active Learning Framework for Cost-Effective TCR-Epitope Binding Affinity Prediction

https://github.com/leibinghe/GAAL-based-outlier-detection

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Generative Adversarial Active Learning for Unsupervised Outlier Detection

https://github.com/lenarttreven/dgm

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Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models

https://github.com/lfilstro/hitl-abc

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Approximate Bayesian Computation with Domain Expert in the Loop

https://github.com/lgordon99/rhino-midden-detector

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Find Rhinos without Finding Rhinos: Active Learning with Multimodal Imagery of South African Rhino Habitats

https://github.com/liangjiubujiu/ctooth

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CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation

https://github.com/licongguan/ilm-assl

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Iterative Loop Method Combining Active and Semi-Supervised Learning for Domain Adaptive Semantic Segmentation

https://github.com/lighterswang/awesome-active-learning-for-medical-image-analysis

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A comprehensive survey on deep active learning in medical image analysis

https://github.com/linkon87/exploring-diversity-based-active-learning-for-3d-object-detection-in-autonomous-driving

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Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving

https://github.com/liu-jc/lscale

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LSCALE: Latent Space Clustering-Based Active Learning for Node Classification

https://github.com/liushangnoname/uncertainty-sampling

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Understanding Uncertainty Sampling

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