https://github.com/dflemin3/approxposterior
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions
Papers with Code 아카이브
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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/dhaalves/CEAL_keras
Cost-Effective Active Learning for Deep Image Classification
https://github.com/englhardt/OneClassActiveLearning.jl
An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers
https://github.com/englhardt/SVDD.jl
An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers
https://github.com/esa/AnomalyMatch
AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning
https://github.com/eugene-yang/tarexp
https://github.com/euphoria0-0/Learning-Loss-for-Active-Learning
https://github.com/felixopolka/ggp-tf2
Bayesian Semi-supervised Learning with Graph Gaussian Processes
https://github.com/fidelity/mab2rec
https://github.com/fidelity/selective
https://github.com/flowersteam/teachDeepRL
Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in Robots
https://github.com/freedombenLiu/ad_examples
https://github.com/frkl/active-learning
Active Learning for Visual Question Answering: An Empirical Study
https://github.com/gauthamkrishna-g/ActiveHARNet
ActiveHARNet: Towards On-Device Deep Bayesian Active Learning for Human Activity Recognition
https://github.com/gd-zhang/noisy-K-FAC
https://github.com/genedisco/genedisco-starter
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
https://github.com/gentopia-ai/gentopia
https://github.com/glard/Active-Generative-Design
Active learning based generative design for the discovery of wide bandgap materials
https://github.com/gortizji/product_graphs
https://github.com/gregory-kyro/chemspaceal
https://github.com/hackerxiaobai/OpenTag_2019
https://github.com/hillup/active_learning
Active Learning for Convolutional Neural Networks: A Core-Set Approach
https://github.com/ielab/ai-review
DenseReviewer: A Screening Prioritisation Tool for Systematic Review based on Dense Retrieval
https://github.com/isayev/ANI1_dataset
https://github.com/jaesik817/bmaml
https://github.com/jaesik817/bmaml_rl
https://github.com/jarl93/joeynmt-modified
https://github.com/jingxixu/Kitchen2D---stir
Active model learning and diverse action sampling for task and motion planning
https://github.com/johntiger1/csc2547-project
https://github.com/johntiger1/vaal
https://github.com/johntiger1/vaal_querying
https://github.com/jonhue/transductive-active-learning
https://github.com/julesberman/colora
Neural Galerkin Schemes with Active Learning for High-Dimensional Evolution Equations
https://github.com/kaleidophon/nlp-uncertainty-zoo
https://github.com/kit-dbis/ocal-evaluation
An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers
https://github.com/kpriyadarshini/BatchAML_Decorrelation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.