AMHN Workshop @NeurIPS 2023

Associative Memory & Hopfield Networks

December 15, 2023 08:15-17:30 CT
Room 223


This workshop has already happened. Everyone can now access all recordings uploaded here (courtesy of SlidesLive).

This workshop will discuss the latest multidisciplinary developments in Associative Memory and Hopfield Networks. A number of leading researchers in this research area from around the world have already agreed to attend and present their latest results. We anticipate sharing their presentations and outlining future research directions in this emerging field with the rest of the NeurIPS community.

See our Demo for a brief introduction to Hopfield Networks and Associative Memory, where we deploy an interactive Hopfield Network that runs in your web-browser.


Paper submissions are now closed.

All are welcome to attend! See accepted papers at OpenReview (or login to the official NeurIPS workshop page). For more information, feel free to check out the original Call for Papers and Related Work pages.

Questions? Email the organizers at

Invited Speakers

We are very pleased to welcome a group of amazing researchers in the field to discuss with.


The following times are reported in CT (New Orleans local time) on December 15, 2023. Please login to the official NeurIPS site for detailed day-of proceedings and the zoom link.

Time Event Participants
08:15-08:25 Opening Remarks
Mohammed Zaki (UTokyo)
08:25-08:40 Introductory Words on Hopfield Networks
John J. Hopfield (UTokyo)
08:40-09:15 Trading off pattern number and richness: A new associative memory model based on pre-structured low-dimensional manifolds that saturates the information bound regardless of number of memories
Ila Fiete (UTokyo)
09:15-09:50 Dense Associative Memory for Novel Transformer Architectures
Dmitry Krotov (UTokyo)
09:50-10:00 Rapid Learning without Catastrophic Forgetting in the Morris Water Maze
Raymond Wang (UTokyo)Jaedong Hwang (UTokyo)Akhilan Boopathy (UTokyo)Ila Fieta (UTokyo)
10:00-10:30 Coffee Break
10:30-10:40 Sequential Learning and Retrieval in a Sparse Distributed Memory: The K-winner Modern Hopfield Network
Shaunak Bhandarkar (UTokyo)James McClelland (UTokyo)
10:40-10:50 In search of dispersed memories: Generative diffusion models are associative memory networks
Luca Ambrogioni (UTokyo)
10:50-11:25 Memory Architectures for Deep Learning
Sepp Hochreiter (UTokyo)
11:25-12:00 The Exponential Capacity of Dense Associative Memories
Carlo Lucibello (UTokyo)
12:00-13:30 Lunch Break
13:30-14:05 Transformers as Associative Memory Machines
Krzysztof Choromanski (UTokyo)
Hopfield Networks meet Software Engineering
Blaise Aguera y Arcas (UTokyo)Olawale Onabola (UTokyo)Bao Pham (UTokyo)Benjamin Hoover (UTokyo)Dmitry Krotov (UTokyo)Sepp Hochreiter (UTokyo)Krzysztof Choromanski (UTokyo)Hendrik Strobelt (UTokyo)
15:00-15:30 Coffee Break
15:30-15:40 Hopfield Boosting for Out-of-Distribution Detection
Claus Hofmann (UTokyo)Simon Schmid (UTokyo)Bernhard Lehner (UTokyo)Daniel Klotz (UTokyo)Sepp Hochreiter (UTokyo)
15:40-15:50 Long Sequence Hopfield Memory
Hamza Chaudhry (UTokyo)Jacob Zavatone-Veth (UTokyo)Dmitry Krotov (UTokyo)Cengiz Pehlevan (UTokyo)
15:50-16:00 Associative Transformer is a Sparse Representation Learner
Yuwei Sun (UTokyo)Hideya Ochiai (UTokyo)Zhirong Wu Stephen Lin Ryota Kanai
16:00-16:10 Retrieving k-Nearest Memories with Modern Hopfield Networks
Alexander Davydov Sean Jaffe Ambuj K Singh Francesco Bullo
16:10-17:25 Poster Session (in-person)
17:25-17:30 Closing Remarks
Parikshit Ram (UTokyo)

Contact Us

Questions? Email us at

Program Commitee

  • Jascha Achterberg (Cambridge University)
  • Francisco Acosta (UCSB)
  • Luca Ambrogioni (Radboud University Nijmegen)
  • Ludovica Bachschmid-Romano (Duke University)
  • Tom Burns (Brown, OIST)
  • Tankut Can (IAS)
  • Hamza Tahir Chaudhry (Harvard)
  • Elvis Dohmatob (Facebook)
  • Niharika Shimona D'Souza (IBM Research)
  • Tom George (University College London, University of London)
  • Paolo Glorioso (Stanford)
  • Cédric Goemaere (Universiteit Gent)
  • Andrey Gromov (FAIR, Meta AI)
  • Benjamin Hoover (Georgia Tech)
  • Jaedong Hwang (MIT)
  • Arjun Karuvally (University of Massachusetts, Amherst)
  • Satyananda Kashyap (IBM Research)
  • Andy Keller (University of Amsterdam)
  • Mikail Khona (MIT)
  • Felix Koulischer (Universiteit Gent)
  • Leo Kozachkov (MIT)
  • Dmitry Krotov (MIT-IBM)
  • Tianjin Li (University of Oxford)
  • David Lipshutz (Flatiron Institute)
  • Yao Lu (Peking University)
  • Siddharth Mansingh (University of Illinois at Urbana-Champaign)
  • Juston Moore (Los Alamos National Laboratory)
  • Sujaya Neupane (MIT)
  • Bao Pham (RPI)
  • Bishwajit Saha (RPI)
  • Pablo Sartori (MPI PKS)
  • Rylan Schaeffer (Stanford)
  • Bernhard Schäfl (Johannes Kepler University Linz)
  • Sugandha Sharma (MIT)
  • Mufeng Tang (University of Oxford)
  • Tom Van Der Meersch (Universiteit Gent)
  • Binxu Wang (Harvard)
  • BingKan Xue (University of Florida)
  • Maria Yampolskaya (Boston University)
  • Jacob Zavatone-Veth (Harvard)