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Additive models

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

https://github.com/udellgroup/controlburn

공식 코드

ControlBurn: Nonlinear Feature Selection with Sparse Tree Ensembles

https://github.com/udellgroup/dnamite

공식 코드

DNAMite: Interpretable Calibrated Survival Analysis with Discretized Additive Models

https://github.com/vatass/adaptiveshrinkagedkgp

공식 코드

Adaptive Shrinkage Estimation For Personalized Deep Kernel Regression In Modeling Brain Trajectories

https://github.com/vduong143/CAT-KDD-2024

공식 코드

CAT: Interpretable Concept-based Taylor Additive Models

https://github.com/vingioia90/scm

공식 코드

Additive Covariance Matrix Models: Modelling Regional Electricity Net-Demand in Great Britain

https://github.com/wei2624/gpnam

공식 코드

Gaussian Process Neural Additive Models

https://github.com/xingyan-fml/emq

공식 코드

Ensemble Multi-Quantiles: Adaptively Flexible Distribution Prediction for Uncertainty Quantification

https://github.com/yinlou/mltk

공식 코드

Sparse Partially Linear Additive Models

https://github.com/yu-group/imodels-experiments

공식 코드

Fast Interpretable Greedy-Tree Sums

https://github.com/yunzhe-zhou/sugar

공식 코드

Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning

https://github.com/ziqi-li/geoshapley

공식 코드

GeoShapley: A Game Theory Approach to Measuring Spatial Effects in Machine Learning Models

https://github.com/zzzace2000/GAMs

공식 코드

How Interpretable and Trustworthy are GAMs?

https://github.com/zzzace2000/nodegam

공식 코드

NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning

https://github.com/AmrMKayid/nam

Neural Additive Models: Interpretable Machine Learning with Neural Nets

https://github.com/Habush/nam_jax

Neural Additive Models: Interpretable Machine Learning with Neural Nets

https://github.com/HelmholtzAI-Consultants-Munich/PySDDR

Semi-Structured Distributional Regression -- Extending Structured Additive Models by Arbitrary Deep Neural Networks and Data Modalities

https://github.com/ak24watch/interpretable-GNNs

The Interpretable and Effective Graph Neural Additive Networks

https://github.com/cohortshapley/cohortintgrad

Deletion and Insertion Tests in Regression Models

https://github.com/duvenaud/additive-gps

Additive Gaussian Processes

https://github.com/enouenj/sparse-interaction-additive-networks

NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning

https://github.com/givasile/effector

Regionally Additive Models: Explainable-by-design models minimizing feature interactions

https://github.com/google-research/google-research/tree/master/neural_additive_models

Neural Additive Models: Interpretable Machine Learning with Neural Nets

https://github.com/jettify/ibreakdown

Do Not Trust Additive Explanations

https://github.com/kherud/neural-additive-models-pt

Neural Additive Models: Interpretable Machine Learning with Neural Nets

https://github.com/krishnap25/trfa

Robust Aggregation for Federated Learning

https://github.com/microsoft/automated-explanations

Augmenting Interpretable Models with LLMs during Training

https://github.com/microsoft/promptbase

InterpretML: A Unified Framework for Machine Learning Interpretability

https://github.com/mlr-org/mlr

Benchmarking time series classification -- Functional data vs machine learning approaches

https://github.com/nickfrosst/neural_additive_models

Neural Additive Models: Interpretable Machine Learning with Neural Nets

https://github.com/npusteln/HierarchicalLearning

Sparse hierarchical interaction learning with epigraphical projection

https://github.com/saipradeep-peri/Neural_Additive_Models_Walkthrough

Neural Additive Models: Interpretable Machine Learning with Neural Nets

https://github.com/selfexplainml/piml-toolbox

GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.