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Transformers are one of the most successful architectures of modern neural networks.
Deep Equilibrium Models, October 2019
Shaojie Bai, J. Zico Kolter, and Vladlen Koltun · 1909
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Statistical Analysis of Non-Lattice Data
Julian Besag · 1975
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Hopfield Networks is All You Need, April 2021
Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich, Thomas Adler, Lukas Gruber, Markus Holzleitner, Milena Pavlović, Geir Kjetil Sandve, Victor Greiff, David Kreil, Michael Kopp, Günter Klambauer, Johannes Brandstetter, and Sepp Hochreiter · 2008
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, June 2021
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2010
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Layer normalization, 2016
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Attention is All you Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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On a Model of Associative Memory with Huge Storage Capacity
Mete Demircigil, Judith Heusel, Matthias Löwe, Sven Upgang, and Franck Vermet · 2017
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Inverse statistical problems: from the inverse Ising problem to data science
H. Chau Nguyen, Riccardo Zecchina, and Johannes Berg · 2017
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Overparameterized neural networks implement associative memory
Adityanarayanan Radhakrishnan, Mikhail Belkin, and Caroline Uhler · 2020
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Highly accurate protein structure prediction with AlphaFold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis · 2021
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Deep implicit attention: A mean-field theory perspective on attention mechanisms, May 2021
Matthias Bal · 2021
Cited alongside, same era.
Transformers are secretly collectives of spin systems, November 2021
Matthias Bal · 2021
Transformers from an Optimization Perspective, February 2023
Yongyi Yang, Zengfeng Huang, and David Wipf · 2023
Later among the works it cites.
Spin-model transformers, December 2023
Matthias Bal · 2023
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Mapping of attention mechanisms to a generalized Potts model
Riccardo Rende, Federica Gerace, Alessandro Laio, and Sebastian Goldt · 2024
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Exponential Capacity of Dense Associative Memories
Carlo Lucibello and Marc Mézard · 2024
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Random Features Hopfield Networks generalize retrieval to previously unseen examples, July 2024
Silvio Kalaj, Clarissa Lauditi, Gabriele Perugini, Carlo Lucibello, Enrico M. Malatesta, and Matteo Negri · 2024
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Energy Transformer, October 2023
Benjamin Hoover, Yuchen Liang, Bao Pham, Rameswar Panda, Hendrik Strobelt, Duen Horng Chau, Mohammed J. Zaki, and Dmitry Krotov · 2023
Cited alongside, same era.
Spontaneous symmetry breaking in generative diffusion models
Gabriel Raya and Luca Ambrogioni · 2023
Cited alongside, same era.
In preparation
Flavio Nicoletti and Matteo Negri
Cited in the paper.
In Search of Dispersed Memories: Generative Diffusion Models Are Associative Memory Networks
Luca Ambrogioni · 2024
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Simplifying Transformer Blocks, May 2024
Bobby He and Thomas Hofmann · 2024
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