Bayesian neural network generalised additive models
In recent years, neural networks (NNs) have gained wide and lasting traction as the machine learning architecture of choice in many contexts, due to its flexibility and ability to represent complex functions. However, in the context of a regression task, NNs face difficulties in interpretability and...
Saved in:
Main Author: | Tay, Caleb Wei Hua |
---|---|
Other Authors: | Xiang Liming |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/172098 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
SCAD-penalised generalised additive models with non-polynomial dimensionality
by: Li, Gaorong, et al.
Published: (2013) -
Probabilistic graphical models : bayesian networks
by: Chan, Xiang Yun
Published: (2021) -
Graphical models and variational Bayesian inference for financial networks
by: Xin, Luyin
Published: (2019) -
Bayesian quantile regression for semiparametric models
by: Hu, Yuao
Published: (2013) -
Efficient algorithms for Bayesian semi-parametric regression models
by: Zhao Kaifeng
Published: (2015)