I consider predictive processing (PP) from the perspective of an artist who also conducts
scientific research into art and perception. This paper presents artworks I have made and …

Generative deep learning systems offer powerful tools for artefact generation, given their
ability to model distributions of data and generate high-fidelity results. In the context of …

We address the problem of synthesizing multi-view optical illusions: images that change
appearance upon a transformation such as a flip or rotation. We propose a simple zero-shot …

Given a factorization of an image into a sum of linear components, we present a zero-shot
method to control each individual component through diffusion model sampling. For …

In this chapter, we describe how the individual's active involvement with works of art
contributes to the experience of them. Based on a review of recent experimental work, we …

Visual ambiguity plays a key role in the perceptual experience of art and has been much
exploited by modernist and contemporary artists for aesthetic effects. But it remains unclear …

Machine learning (ML) techniques are increasingly being employed within a variety of
creative domains. For example, ML tools are being used to analyze the authenticity of …

In the last few years, a remarkable convergence of interests and results has emerged
between scholars interested in the arts and aesthetics from a variety of perspectives and …

This paper describes how computational generative models can describe aspects of the
artistic process, and how these generative models can provide tools for formulating and …

Algorithmic recommendation is one of the most popular applications of machine learning
(ML) systems. While the implication of algorithmic recommendation has been studied in the …