# Classified as unknown: A novel Bayesian neural network

arXiv: https://arxiv.org/abs/2301.13401 · HTML: https://mlsanity.com/paper/2301.13401/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 31 Jan 2023.

## Builds on (most cited first, 7 of 7)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Weight Uncertainty in Neural Networks](https://mlsanity.com/paper/1505.05424/) (2015)
- ['In-Between' Uncertainty in Bayesian Neural Networks](https://mlsanity.com/paper/1906.11537/) (2019)
- [An Exploration of Softmax Alternatives Belonging to the Spherical Loss Family](https://mlsanity.com/paper/1511.05042/) (2015)
- [Exploring Alternatives to Softmax Function](https://mlsanity.com/paper/2011.11538/) (2020)
- [Bayesian Perceptron: Towards fully Bayesian Neural Networks](https://mlsanity.com/paper/2009.01730/) (2020)

## Abstract (arXiv)

We establish estimations for the parameters of the output distribution for the softmax activation function using the probit function. As an application, we develop a new efficient Bayesian learning algorithm for fully connected neural networks, where training and predictions are performed within the Bayesian inference framework in closed-form. This approach allows sequential learning and requires no computationally expensive gradient calculation and Monte Carlo sampling. Our work generalizes the Bayesian algorithm for a single perceptron for binary classification in citeH to multi-layer perceptrons for multi-class classification.