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onnx
onnx:R Interface to 'ONNX'
R Interface to 'ONNX' - Open Neural Network Exchange <https://onnx.ai/>. 'ONNX' provides an open source format for machine learning models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.
Maintained by Yuan Tang. Last updated 2 years ago.
deep-learningdeep-neural-networksonnx
44 stars 5.90 score 18 scripts
inesortega
neuralGAM:Interpretable Neural Network Based on Generalized Additive Models
Neural network framework based on Generalized Additive Models from Hastie & Tibshirani (1990, ISBN:9780412343902), which trains a different neural network to estimate the contribution of each feature to the response variable. The networks are trained independently leveraging the local scoring and backfitting algorithms to ensure that the Generalized Additive Model converges and it is additive. The resultant Neural Network is a highly accurate and interpretable deep learning model, which can be used for high-risk AI practices where decision-making should be based on accountable and interpretable algorithms.
Maintained by Ines Ortega-Fernandez. Last updated 7 months ago.
deep-neural-networksexplainable-aigamganngeneralized-additive-modelsgeneralized-additive-neural-networkself-explanatory-mlxai
2 stars 5.44 score 40 scripts
hknd23
DeepLearningCausal:Causal Inference with Super Learner and Deep Neural Networks
Functions to estimate Conditional Average Treatment Effects (CATE) and Population Average Treatment Effects on the Treated (PATT) from experimental or observational data using the Super Learner (SL) ensemble method and Deep neural networks. The package first provides functions to implement meta-learners such as the Single-learner (S-learner) and Two-learner (T-learner) described in Künzel et al. (2019) <doi:10.1073/pnas.1804597116> for estimating the CATE. The S- and T-learner are each estimated using the SL ensemble method and deep neural networks. It then provides functions to implement the Ottoboni and Poulos (2020) <doi:10.1515/jci-2018-0035> PATT-C estimator to obtain the PATT from experimental data with noncompliance by using the SL ensemble method and deep neural networks.
Maintained by Nguyen K. Huynh. Last updated 2 months ago.
causal-inferencedeep-neural-networksmachine-learning
2 stars 4.73 score 5 scripts