Ben Hoover

So this blew up. Probs good to add clarification to @DimaKrotov's quote "Computation is a physical process. We can study the flow of bits just as we study the flow of atoms" This has always been the perspective of Hopfield Nets. Buckle up here's a primer on Associative Memory🧵

I am frequently asked about the difference between binary and continuous Hopfield networks. Binary networks operate on discrete spins that are flipped in random order, continuous ones are described by differential equations and continuous state vectors. What is the right way to…

How to build a factual but creative system? It is a question surrounding memory and creativity in modern ML systems. My colleagues from @IBMResearch and @MITIBMLab are hosting the @MemVis_ICCV25 workshop at #ICCV2025, which explores the intersection between memory and generative…

1/3) I am biased, but I think this is going to be big! CoVAE: Consistency Training of Variational Autoencoders We unify consistency models with VAEs to obtain a powerful and elegant generative autoencoder! The brainchild of the brilliant @gisilvs (who is looking for jobs!)

The official ICML 2025 paper browser is hot off the press! Enjoy using it and discover new papers and clusters of research. icml2025.vizhub.ai

I threw together a short paper outlining our tool Diffusion Explorer. Diffusion Explorer allows you to learn about the geometric properties of diffusion and flow based generative models with interactive animation.

Lagrangians are often used in physics for deriving the energy of mechanical systems. But are they useful for neural networks and AI? It turns out they are extremely helpful for working with energy-based models and energy-based Associative Memories. You need to specify a…

In physics there is an elegant method for computing the correlation functions called generating function. The idea is simple - instead of computing correlators one by one - you define a function of a parameter and compute the average of that new function. Individual correlators…