2019/10/06 by Terence Broad, Broad, Terence, Mick Grierson +1
Computer Science · Physics and Astronomy · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Model Reduction and Neural Networks #cs.AI #cs.LG
paper · pdf · doi:10.48550/arxiv.1910.02409
arxiv created 2019/10/06 · openalex publication_date 2019/10/06 · arxiv updated 2019/10/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper details a developing artistic practice around an ongoing series of works called (un)stable equilibrium. These works are the product of using modern machine toolkits to train generative models without data, an approach akin to traditional generative art where dynamical systems are explored intuitively for their latent generative possibilities. We discuss some of the guiding principles that have been learnt in the process of experimentation, present details of the implementation of the first series of works and discuss possibilities for future experimentation.