Dmitry Krotov

@krotov.bsky.social

I am a physicist working on neural networks (artificial and biological). Find me on dmitrykrotov.com.

New paper 🧵 What if the "landscape" that guides cell differentiation isn't fixed, but is sculpted, in real time, by the very cells moving through it? We call this a 'sandscape' : grains of sand that carve dunes as they move, while the dunes' shape simultaneously steers where the grains go next.

Tremendously excited to announce that I will be joining @rockefeller.edu as an Assistant Professor and Head of Lab starting in January 2027! My group will be broadly focused on theoretical neuroscience, and mathematical problems in neural computation in the large.

Rockefeller campus image from https://commons.wikimedia.org/wiki/File:Rockefeller_University_Campus_aerial_2.jpg, licensed under the Creative Commons Attribution-Share Alike 2.5 Generic license.

I have just landed in San Diego and I will be at #NeurIPS2025 for the week. DM or send me an email if you want to chat about interesting papers and potential projects related to associative memory, energy-based models, new architectures, or other cool ideas.

Bild

Big week for astrocyte research: 3 new Science papers link astrocytes to behavior. We're excited to add to the momentum with our new PNAS paper: a theory, grounded in biology, proposing astrocytes as key players in memory storage and recall. w/ JJ Slotine and @krotov.bsky.social (1/6)

Bild

I am heading to #ICLR2025 in Singapore. If you want to chat about associative memories, energy-based models, and related topics let’s connect! The highlight for me this year is the New Frontiers in Associative Memories workshop on Sunday April 27. Here is the schedule ⬇️

Bild

Now that ICML papers are submitted and we are in the midst of discussions on whether scaling is enough or new architectural/algorithmic ideas are needed, what can be a better time to submit your best work to our workshop on New Frontiers in Associative Memory @iclr-conf.bsky.social?

Bild

Most of the work on Dense Associative Memory (DenseAM) thus far has focused on the regime when the amount of data (number of memories) is below the critical memory storage capacity. We are beginning to explore the opposite limit, when the data is large.

Bao Pham@baopham.bsky.social · 2y ago

Diffusion models create beautiful novel images, but they can also memorize samples from the training set. How does this blending of features allow creating novel patterns? Our new work in Sci4DL workshop #neurips2024 shows that diffusion models behave like Dense Associative Memory networks.

On the left figure, it showcases the behavior of Hopfield models. Given a query (the initial point of energy descent), a Hopfield model will retrieve the closest memory (local minimum) to that query such that it minimizes the energy function. A perfect Hopfield model is able to store patterns in distinct minima (or buckets). In contrast, the right figure illustrates a bad Associative Memory system, where stored patterns share a distinctive bucket. This enables the creation of spurious patterns, which appear like mixture of stored patterns. Spurious patterns will have lower energy than the memories due to this overlapping.

Check out our new #NeurIPS2024 paper, where we develop a distributed representation for a broad class of Dense Associative Memories. Similarly to the traditional Hopfield Network, the number of weights can be kept fixed and independent of the number of memories when new patterns are introduced.

Ben Hoover@bhoov.bsky.social · 2y ago

Excited to share "Dense Associative Memory through the Lens of Random Features" accepted to #neurips2024🎉 DenseAMs need new weights for each stored pattern–hurting scalability. Kernel methods let us add memories without adding weights! Distributed memory for DenseAMs, unlocked🔓