1 citations · 2 across the 5 of their papers we have counts for
6 papers
Using the Projected Belief Network at High Dimensions
Paul M Baggenstoss
The projected belief network (PBN) is a layered generative network (LGN) with tractable likelihood function, and is based on a feed-forward neural network (FFNN). There are two ver…
Trainable Compound Activation Functions for Machine Learning
Paul M. Baggenstoss
Activation functions (AF) are necessary components of neural networks that allow approximation of functions, but AFs in current use are usually simple monotonically increasing func…
Maximum Entropy Auto-Encoding
Paul M Baggenstoss
In this paper, it is shown that an auto-encoder using optimal reconstruction significantly outperforms a conventional auto-encoder. Optimal reconstruction uses the conditional mean…
The Projected Belief Network Classfier : both Generative and Discriminative
Paul M Baggenstoss
The projected belief network (PBN) is a layered generative network with tractable likelihood function, and is based on a feed-forward neural network (FF-NN). It can therefore share…
A Neural Network Based on First Principles
Paul M Baggenstoss
In this paper, a Neural network is derived from first principles, assuming only that each layer begins with a linear dimension-reducing transformation. The approach appeals to the…
Kernel-based Generative Learning in Distortion Feature Space
Bo Tang, Paul M. Baggenstoss, Haibo He
This paper presents a novel kernel-based generative classifier which is defined in a distortion subspace using polynomial series expansion, named Kernel-Distortion (KD) classifier.…