Ben's headshot

Hi, I'm Ben Hoover

I'm an AI Researcher studying memory

Understanding AI foundation models from the perspective of large Associative Memories.

I am a Machine Learning PhD student at Georgia Tech advised by Polo Chau and an AI Research Engineer with IBM Research. My research focuses on building more interpretable and parameter efficient AI by rethinking the way we train and build deep models, taking inspiration from Associative Memories and Hopfield Networks. I like to visualize what happens inside AI models.

News

Oct 2024
πŸŽ‰ Transformer Explainer wins πŸ† Best PosterπŸ₯‡ at IEEE VIS'24!
Sep 2024
πŸŽ‰ "Dense Associative Memory through the Lens of Random Features" accepted (poster) to NeurIPS'24!
Aug 2024
πŸš€ Transformer Explainer is going viral! ( )
Jun 2024
πŸŽ‰ Diffusion Explainer accepted as a VIS'24 Short Paper!
May 2024
β˜•οΈ Invited to speak at Plectics Lab's Colloquium: "Hopfield Networks 2.0: Associative Memory for the Modern Era of AI" (recorded presentation here)
Apr 2024
πŸŽ‰ Diffusion Explainer accepted to the Demo Track at IJCAI 2024!
See more...

Memory Research Highlights

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Memory in Plain Sight

We are the first work to discover that diffusion models perform memory retrieval in their denoising dynamics.
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Energy Transformer

We derive an Associative Memory inspired by the famous Transformer architecture, where the forward pass through the model is memory retrieval by energy descent.
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HAMUX

We invent a software abstraction around "synapses" and "neurons" to assemble energy functions of complicated Associative Memories, where memory retrieval is performed through autograd.

Visualization Research Highlights

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Transformer Explainer

Transformers are the most powerful AI innovation of the last decade. Learn how they work by interacting with every mechanic from the comfort of your web browser. Taught in Georgia Tech CSE6242 Data and Visual Analytics (typically 250-300 students per semester).
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Diffusion Explainer

Diffusion models are complicated. We break down Stable Diffusion and explain each component of the model visually. Taught in Georgia Tech CSE6242 Data and Visual Analytics (typically 250-300 students per semester).
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RXNMapper

We discover that Transformers trained on chemical reactions learn, on their own, how atoms physically rearrange.