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Model Selection

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Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

코드

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

https://CRAN.R-project.org/package=NPflow

공식 코드

Sequential Dirichlet Process Mixtures of Multivariate Skew t-distributions for Model-based Clustering of Flow Cytometry Data

https://bitbucket.org/YanivShu/diffprune_public

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DiffPrune: Neural Network Pruning with Deterministic Approximate Binary Gates and $L_0$ Regularization

https://bitbucket.org/cdal/sia

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Model-based Clustering using Automatic Differentiation: Confronting Misspecification and High-Dimensional Data

https://bitbucket.org/mvdschaar/mlforhealthlabpub

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Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning

https://git.skewed.de/count0/graph-tool

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Hierarchical Block Structures and High-resolution Model Selection in Large Networks

https://github.com/AMLab-Amsterdam/L0_regularization

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Learning Sparse Neural Networks through $L_0$ Regularization

https://github.com/AlexImmer/Laplace

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Laplace Redux -- Effortless Bayesian Deep Learning

https://github.com/AlexImmer/marglik

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Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning

https://github.com/AnastasisKratsios/Deep_Arbitrage_Free_Regularization

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Deep Learning in a Generalized HJM-type Framework Through Arbitrage-Free Regularization

https://github.com/Annie983284450-1/CPGapBandit

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Sepsyn-OLCP: An Online Learning-based Framework for Early Sepsis Prediction with Uncertainty Quantification using Conformal Prediction

https://github.com/Anupreet-Porwal/DP-mix-block-g-prior

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Dirichlet process mixtures of block $g$ priors for model selection and prediction in linear models

https://github.com/BEAST2-Dev/nested-sampling

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Model selection and parameter inference in phylogenetics using Nested Sampling

https://github.com/Battery-Intelligence-Lab/BayesianModelSelection

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Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian Quadrature

https://github.com/Blunde1/aGTBoost

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An information criterion for automatic gradient tree boosting

https://github.com/CAMMA-public/cholect45

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Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets

https://github.com/CGCL-codes/naturalcc

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How to Select Pre-Trained Code Models for Reuse? A Learning Perspective

https://github.com/CPJKU/information_retrieval_fairness_debiasing

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Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation for BERT Rankers

https://github.com/CausalML/DeepGMM

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Deep Generalized Method of Moments for Instrumental Variable Analysis

https://github.com/Cavin-Lee/LZOV

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Leave Zero Out: Towards a No-Cross-Validation Approach for Model Selection

https://github.com/CompBioUIC/CoordinationModelSelectionFramework

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Framework for Inferring Following Strategies from Time Series of Movement Data

https://github.com/CristianCastiglione/sgdGMF

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Stochastic gradient descent estimation of generalized matrix factorization models with application to single-cell RNA sequencing data

https://github.com/DAI-Lab/Cardea

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Cardea: An Open Automated Machine Learning Framework for Electronic Health Records

https://github.com/DIDNN/NPIs

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Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time series

https://github.com/DS3Lab/online-active-model-selection

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Online Active Model Selection for Pre-trained Classifiers

https://github.com/DavidHofmeyr/edfkmeans

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Degrees of Freedom and Model Selection for k-means Clustering

https://github.com/Demodesu/How-False-Data-Affects-Machine-Learning-Models-in-Electrochemistry

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How False Data Affects Machine Learning Models in Electrochemistry?

https://github.com/Djaber1979/-Behavioral-Augmentation-of-UML-Class-Diagrams

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Behavioral Augmentation of UML Class Diagrams: An Empirical Study of Large Language Models for Method Generation

https://github.com/Duchstf/CNN-Architectural-Analysis

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Inferring Convolutional Neural Networks' accuracies from their architectural characterizations

https://github.com/EnriqueSolarte/360-mlc

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360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter Tuning

https://github.com/Evfro/polara

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HybridSVD: When Collaborative Information is Not Enough

https://github.com/Evfro/recsys19_hybridsvd

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HybridSVD: When Collaborative Information is Not Enough

https://github.com/FlorentF9/SOMperf

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A Survey and Implementation of Performance Metrics for Self-Organized Maps

https://github.com/FlorentF9/skstab

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Selecting the Number of Clusters $K$ with a Stability Trade-off: an Internal Validation Criterion

https://github.com/FranxYao/FlanT5-CoT-Specialization

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Specializing Smaller Language Models towards Multi-Step Reasoning

https://github.com/GAA-UAM/scikit-fda

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scikit-fda: A Python Package for Functional Data Analysis

https://github.com/HLT-ISTI/QuaPy

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QuaPy: A Python-Based Framework for Quantification

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