2021/12/31 by Torsten Enßlin, T. A. Enßlin
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Mathematics · Physics and Astronomy · #Artificial intelligence #Artificial neural network #Computer science #Context (archaeology) #Field (mathematics) #Fractal and DNA sequence analysis #Inference #Information theory #Machine learning #Mathematics #Neural Networks and Applications #Probabilistic logic #Statistical Mechanics and Entropy #cs.LG #eess.SP #stat.ML
paper · pdf · doi:10.3390/e24030374
published as Torsten A. Enßlin, Entropy 2022, 24, 374 · 12 pages, three figures, invited talk at MaxEnt2020/2021, reviewed and published by Entropy
arxiv created 2022/03/07 · openalex publication_date 2022/03/07 · arxiv updated 2022/03/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Information field theory (IFT), the information theory for fields, is a mathematical framework for signal reconstruction and non-parametric inverse problems. Artificial intelligence (AI) and machine learning (ML) aim at generating intelligent systems including such for perception, cognition, and learning. This overlaps with IFT, which is designed to address perception, reasoning, and inference tasks. Here, the relation between concepts and tools in IFT and those in AI and ML research are discussed. In the context of IFT, fields denote physical quantities that change continuously as a function of space (and time) and information theory refers to Bayesian probabilistic logic equipped with the associated entropic information measures. Reconstructing a signal with IFT is a computational problem similar to training a generative neural network (GNN) in ML. In this paper, the process of inference in IFT is reformulated in terms of GNN training. In contrast to classical neural networks, IFT based GNNs can operate without pre-training thanks to incorporating expert knowledge into their architecture. Furthermore, the cross-fertilization of variational inference methods used in IFT and ML are discussed. These discussions suggests that IFT is well suited to address many problems in AI and ML research and application.