If you’ve looked at some classic papers in energy-based neural networks (e.g., the Hopfield neural network, the Boltzmann machine, the restricted Boltzmann machine, and all forms of deep learning), you’ll see that they don’t use the word “entropy.” At the same time, we’ve stated that entropy is a fundamental concept in these energy-based neural networks.… Continue reading Entropy in Energy-Based Neural Networks – Seven Key Papers (Part 3 of 3)
Category: Statistical Mechanics
Understanding Entropy: Essential to Mastering Advanced AI
Have you been stumped when trying to read the classic AI papers? Are notions such as free energy and entropy confusing? This is probably because ALL areas of advanced AI are based, to some extent, on statistical mechanics. That means that you need to understand some “stat mech” rudiments to get through those papers. One… Continue reading Understanding Entropy: Essential to Mastering Advanced AI
Latent Variables Enabled Effective Energy-Based Neural Networks: Seven Key Papers (Part 2 of 3)
Latent variables enabled effective energy-based neural networks. The key problem with the Little/Hopfield neural network was its limited memory capacity. This problem was resolved when Hinton, Ackley, and Sejnowski introduced the notion of latent variables, creating the Boltzmann machine. Seven key papers define the evolution of energy-based neural networks. Previously, we examined the first two… Continue reading Latent Variables Enabled Effective Energy-Based Neural Networks: Seven Key Papers (Part 2 of 3)
Seven Key Papers for Energy-Based Neural Networks and Deep Learning (Part 1 of 3)
Seven key papers provide us with the evolutionary timeline for energy-based neural networks, up through and including deep learning. The timeline for these papers begins with William Little’s 1974 work on the first energy-based neural network, and then John Hopfield’s 1982 expansion on Little’s concepts, up through deep learning architectures as described by Hinton and… Continue reading Seven Key Papers for Energy-Based Neural Networks and Deep Learning (Part 1 of 3)