Workshop
Associative Memory & Hopfield Networks in 2023
Parikshit Ram · Hilde Kuehne · Daniel Lee · Cengiz Pehlevan · Mohammed Zaki · Lenka Zdeborová
Room 223
Fri 15 Dec, 6:15 a.m. PST
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.
Tagline: We will discuss recent multidisciplinary developments in Hopfield Networks and outline future research directions in this emerging field.
Chat is not available.
Timezone: America/Los_Angeles
Schedule
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Fri 6:15 a.m. - 6:25 a.m.
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Opening Remarks
(
Opening
)
>
SlidesLive Video |
Mohammed Zaki 🔗 |
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Fri 6:25 a.m. - 6:40 a.m.
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Introductory words on Hopfield Networks
(
Invited Talk
)
>
SlidesLive Video |
John J. Hopfield 🔗 |
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Fri 6:40 a.m. - 7:15 a.m.
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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
(
Invited Talk
)
>
SlidesLive Video |
Ila Fiete 🔗 |
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Fri 7:15 a.m. - 7:50 a.m.
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Dense Associative Memory for Novel Transformer Architectures
(
Invited Talk
)
>
SlidesLive Video |
Dmitry Krotov 🔗 |
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Fri 7:50 a.m. - 8:00 a.m.
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Rapid Learning without Catastrophic Forgetting in the Morris Water Maze
(
Oral
)
>
link
SlidesLive Video |
Raymond Wang · Jaedong Hwang · Akhilan Boopathy · Ila Fiete 🔗 |
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Fri 8:00 a.m. - 8:30 a.m.
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Coffee Break
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🔗 |
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Fri 8:30 a.m. - 8:40 a.m.
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Sequential Learning and Retrieval in a Sparse Distributed Memory: The K-winner Modern Hopfield Network
(
Oral
)
>
link
SlidesLive Video |
Shaunak Bhandarkar · James McClelland 🔗 |
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Fri 8:40 a.m. - 8:50 a.m.
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In search of dispersed memories: Generative diffusion models are associative memory networks
(
Oral
)
>
link
SlidesLive Video |
Luca Ambrogioni 🔗 |
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Fri 8:50 a.m. - 9:25 a.m.
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Memory Architectures for Deep Learning
(
Invited Talk
)
>
SlidesLive Video |
Sepp Hochreiter 🔗 |
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Fri 9:25 a.m. - 10:00 a.m.
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The Exponential Capacity of Dense Associative Memories
(
Invited Talk
)
>
SlidesLive Video |
Carlo Lucibello 🔗 |
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Fri 10:00 a.m. - 11:30 a.m.
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Lunch Break
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🔗 |
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Fri 11:30 a.m. - 12:05 p.m.
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Transformers as the Associative Memory Machines
(
Invited Talk
)
>
SlidesLive Video |
Krzysztof Choromanski 🔗 |
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Fri 12:05 p.m. - 1:00 p.m.
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Hopfield Networks meet Software Engineering
(
Panel Discussion
)
>
SlidesLive Video |
Blaise Aguera y Arcas · Olawale Onabola · Bao Pham · Benjamin Hoover · Hendrik Strobelt 🔗 |
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Fri 1:00 p.m. - 1:30 p.m.
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Coffee Break
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🔗 |
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Fri 1:30 p.m. - 1:40 p.m.
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Hopfield Boosting for Out-of-Distribution Detection
(
Oral
)
>
link
SlidesLive Video |
Claus Hofmann · Simon Schmid · Bernhard Lehner · Daniel Klotz · Sepp Hochreiter 🔗 |
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Fri 1:40 p.m. - 1:50 p.m.
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Long Sequence Hopfield Memory
(
Oral
)
>
link
SlidesLive Video |
Hamza Chaudhry · Jacob Zavatone-Veth · Dmitry Krotov · Cengiz Pehlevan 🔗 |
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Fri 1:50 p.m. - 2:00 p.m.
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Associative Transformer Is A Sparse Representation Learner
(
Oral
)
>
link
SlidesLive Video |
Yuwei Sun · Hideya Ochiai · Zhirong Wu · Stephen Lin · Ryota Kanai 🔗 |
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Fri 2:00 p.m. - 2:10 p.m.
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Retrieving $k$-Nearest Memories with Modern Hopfield Networks
(
Oral
)
>
link
SlidesLive Video |
Alexander Davydov · Sean Jaffe · Ambuj K Singh · Francesco Bullo 🔗 |
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Fri 2:10 p.m. - 3:25 p.m.
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Poster Session
(
In-person Poster Session
)
>
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🔗 |
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Fri 3:25 p.m. - 3:30 p.m.
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Closing Remarks
(
Conclusion
)
>
SlidesLive Video |
Parikshit Ram 🔗 |
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Sparse Modern Hopfield Networks ( Poster ) > link | André Martins · Vlad Niculae · Daniel McNamee 🔗 |
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Controlling the bifurcations of attractors in modern Hopfield networks ( Poster ) > link | Maria Yampolskaya · Pankaj Mehta 🔗 |
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Associative Transformer Is A Sparse Representation Learner ( Poster ) > link | Yuwei Sun · Hideya Ochiai · Zhirong Wu · Stephen Lin · Ryota Kanai 🔗 |
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Long Sequence Hopfield Memory ( Poster ) > link | Hamza Chaudhry · Jacob Zavatone-Veth · Dmitry Krotov · Cengiz Pehlevan 🔗 |
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Training a Hopfield Variational Autoencoder with Equilibrium Propagation ( Poster ) > link | Tom Van Der Meersch · Johannes Deleu · Thomas Demeester 🔗 |
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In-Context Exemplars as Clues to Retrieving from Large Associative Memory ( Poster ) > link | Jiachen Zhao 🔗 |
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Enhanced cue associated memory in temporally consistent recurrent neural networks ( Poster ) > link | Udith Haputhanthri · Liam Storan · Adam Shai · Surya Ganguli · Mark Schnitzer · Hidenori Tanaka · Fatih Dinc 🔗 |
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Learning Sequence Attractors in Recurrent Networks with Hidden Neurons ( Poster ) > link | Yao Lu · Si Wu 🔗 |
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Associative Memory Under the Probabilistic Lens: Improved Transformers & Dynamic Memory Creation ( Poster ) > link | Rylan Schaeffer · Mikail Khona · Nika Zahedi · Ila Fiete · Andrey Gromov · Sanmi Koyejo 🔗 |
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In search of dispersed memories: Generative diffusion models are associative memory networks ( Poster ) > link | Luca Ambrogioni 🔗 |
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Memorization and consolidation in associative memory networks ( Poster ) > link | Danil Tyulmankov · Kimberly Stachenfeld · Dmitry Krotov · L F Abbott 🔗 |
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Modeling Recognition Memory with Predictive Coding and Hopfield Networks ( Poster ) > link | Tianjin Li · Mufeng Tang · Rafal Bogacz 🔗 |
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Associative Memories with Heavy-Tailed Data ( Poster ) > link | Vivien Cabannes · Elvis Dohmatob · Alberto Bietti 🔗 |
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Inverse distance weighting attention ( Poster ) > link | Calvin McCarter 🔗 |
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Modern Hopfield Networks as Memory for Iterative Learning on Tabular Data ( Poster ) > link | Bernhard Schäfl · Lukas Gruber · Angela Bitto · Sepp Hochreiter 🔗 |
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Random Feature Hopfield Networks generalize retrieval to previously unseen examples ( Poster ) > link | Matteo Negri · Clarissa Lauditi · Gabriele Perugini · Carlo Lucibello · Enrico Malatesta 🔗 |
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Retrieving $k$-Nearest Memories with Modern Hopfield Networks ( Poster ) > link | Alexander Davydov · Sean Jaffe · Ambuj K Singh · Francesco Bullo 🔗 |
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A Different Route to Exponential Storage Capacity ( Poster ) > link | Elvis Dohmatob 🔗 |
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Variable Memory: Beyond the Fixed Memory Assumption in Memory Modeling ( Poster ) > link | Arjun Karuvally · Hava Siegelmann 🔗 |
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Biologically-inspired adaptive learning in the Hopfield-network based self-optimization model ( Poster ) > link | Aisha Belhadi 🔗 |
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How Robust Are Energy-Based Models Trained With Equilibrium Propagation? ( Poster ) > link | Siddharth Mansingh · Michal Kucer · Garrett Kenyon · Juston Moore · Michael Teti 🔗 |
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Accelerating Hierarchical Associative Memory: A Deep Equilibrium Approach ( Poster ) > link | Cédric Goemaere · Johannes Deleu · Thomas Demeester 🔗 |
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Sequential Learning and Retrieval in a Sparse Distributed Memory: The K-winner Modern Hopfield Network ( Poster ) > link | Shaunak Bhandarkar · James McClelland 🔗 |
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Energy Transformer ( Poster ) > link | Benjamin Hoover · Yuchen Liang · Bao Pham · Rameswar Panda · Hendrik Strobelt · Duen Horng Chau · Mohammed Zaki · Dmitry Krotov 🔗 |
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Modulating interactions to control dynamics of neural networks ( Poster ) > link | Lukas Herron · Pablo Sartori · BingKan Xue 🔗 |
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Generalizable Relational Inference with Cognitive Maps in a Hippocampal Model and in Primates ( Poster ) > link | Jaedong Hwang · Sujaya Neupane · Mehrdad Jazayeri · Ila Fiete 🔗 |
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Modern Hopfield Network with Local Learning Rules for Class Generalization ( Poster ) > link | Shruti Joshi · Giri Prashanth · Maksim Bazhenov 🔗 |
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Rapid Learning without Catastrophic Forgetting in the Morris Water Maze ( Poster ) > link | Raymond Wang · Jaedong Hwang · Akhilan Boopathy · Ila Fiete 🔗 |
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Multidimensional Hopfield Networks for clustering ( Poster ) > link | Gergely Stomfai · Łukasz Sienkiewicz · Barbara Rychalska 🔗 |
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Statistics-guided Associative Memories ( Poster ) > link | Hongzhi Wang · Satyananda Kashyap · Niharika DSouza · Tanveer Syeda-Mahmood 🔗 |
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Daydreaming Hopfield Networks and their surprising effectiveness on correlated data ( Poster ) > link | Ludovica Serricchio · Claudio Chilin · Dario Bocchi · Raffaele Marino · Matteo Negri · Chiara Cammarota · Federico Ricci-Tersenghi 🔗 |
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Hopfield Boosting for Out-of-Distribution Detection ( Poster ) > link | Claus Hofmann · Simon Schmid · Bernhard Lehner · Daniel Klotz · Sepp Hochreiter 🔗 |
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Saliency-Guided Hidden Associative Replay for Continual Learning ( Poster ) > link | Guangji Bai · Qilong Zhao · Xiaoyang Jiang · Liang Zhao 🔗 |
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Skip Connections Increase the Capacity of Associative Memories in Variable Binding Mechanisms ( Poster ) > link | Yi Xie · Yichen Li · Akshay Rangamani 🔗 |
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Minimum Description Length Hopfield Networks ( Poster ) > link | Matan Abudy · Nur Lan · Emmanuel Chemla · Roni Katzir 🔗 |
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Memory in Plain Sight: A Survey of the Uncanny Resemblances between Diffusion Models and Associative Memories ( Poster ) > link | Benjamin Hoover · Hendrik Strobelt · Dmitry Krotov · Judy Hoffman · Zsolt Kira · Duen Horng Chau 🔗 |
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Exploring the Temperature-Dependent Phase Transition in Modern Hopfield Networks ( Poster ) > link | Felix Koulischer · Cédric Goemaere · Tom Van Der Meersch · Johannes Deleu · Thomas Demeester 🔗 |
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Probabilistic Forecasting via Modern Hopfield Networks ( Poster ) > link | Kashif Rasul · Pablo Vicente · Anderson Schneider · Alexander März 🔗 |
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Error-correcting columnar networks: high-capacity memory under sparse connectivity ( Poster ) > link | Haozhe Shan · Ludovica Bachschmid-Romano · Haim Sompolinsky 🔗 |
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Hopfield-Enhanced Deep Neural Networks for Artifact-Resilient Brain State Decoding ( Poster ) > link | Arnau Marin-Llobet · Arnau Manasanch · Mavi Sanchez-Vives 🔗 |