We present a review of predictive coding, from theoretical neuroscience, and variational
autoencoders, from machine learning, identifying the common origin and mathematical …

One of the aims of the 4th industrial revolution is to seamlessly connect equipment and
personnel to enable a greater level of collaboration, which in turn will result in higher …

We present a sparse representation of model uncertainty for Deep Neural Networks (DNNs)
where the parameter posterior is approximated with an inverse formulation of the …

While the impact of variational inference (VI) on posterior inference in a fixed generative
model is well-characterized, its role in regularizing a learned generative model when used …

Established methods for unsupervised representation learning such as variational
autoencoders produce none or poorly calibrated uncertainty estimates making it difficult to …

Abstract Variational Autoencoders (VAEs) suffer from degenerated performance, when
learning several successive tasks. This is caused by catastrophic forgetting. In order to …

We combine two popular optimization approaches to derive learning algorithms for
generative models: variational optimization and evolutionary algorithms. The combination is …

We propose an efficient solver for the privacy funnel (PF) method, leveraging its difference-of-
convex (DC) structure. The proposed DC separation results in a closed-form update …

Network Intrusion Detection System (NIDS) is an important tool for network administrators to
detect security breaches in a network. However, due to the diversity of attacks and …

High dimensional data is often assumed to be concentrated on or near a low-dimensional
manifold. Autoencoders (AE) is a popular technique to learn representations of such data by …