Accelerated Bayesian inference using deep learning

Accelerated Bayesian Optimization for Deep Learning

Bayesian optimization for deep learning has extensive execution time because it involves several calculations and parameters. To solve this problem, this study aims at accelerating the execution time by focusing on the output of the activation function that is strongly related to accuracy. We developed a technique to accelerate the execution time by stopping the learning model so that the activ...

An Introduction to Inference and Learning in Bayesian Networks

Bayesian networks (BNs) are modern tools for modeling phenomena in dynamic and static systems and are used in different subjects such as disease diagnosis, weather forecasting, decision making and clustering. A BN is a graphical-probabilistic model which represents causal relations among random variables and consists of a directed acyclic graph and a set of conditional probabilities. Structure...

Bayesian Deep Learning | Uncertainty in Deep Learning

Learning Deep Inference Machines

Introduction. The traditional approach to structured prediction problems is to craft a graphical model structure, learn parameters for the model, and perform inference using an efficient– and usually approximate– inference approach, including, e.g., graph cut methods, belief propagation, and variational methods. Unfortunately, while remarkably powerful methods for inference have been developed ...

Interpreting Deep Classification Models with Bayesian Inference

In this paper, we propose a novel approach to interpret a well-trained classification model through systematically investigating effects of its hidden units on prediction making. We search for the core hidden units responsible for predicting inputs as the class of interest under the generative Bayesian inference framework. We model such a process of unit selection as an Indian Buffet Process, a...