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The generative process of Diffusion Models (DMs) has recently set state-of-the-art on many AI generation benchmarks.
Content-Addressable and Associative Memory Systems a Survey
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Neural networks and physical systems with emergent collective computational abilities
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Neurons With Graded Response Have Collective Computational Properties Like Those of Two-State Neurons
John Hopfield · 1984
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Boltzmann Machines: Constraint Satisfaction Networks That Learn
Geoffrey E Hinton, Terrence J Sejnowski, and David H Ackley · 1984
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Information capacity of the Hopfield model
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Daniel J. Amit, Hanoch Gutfreund, and H. Sompolinsky · 1985
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A learning algorithm for Boltzmann machines
David H Ackley, Geoffrey E Hinton, and Terrence J Sejnowski · 1985
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Sparse Distributed Memory
Pentti Kanerva · 1988
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An alternative design for a sparse distributed memory
Louis A. Jaeckel · 1989
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On the limited memory BFGS method for large scale optimization
Dong C. Liu and Jorge Nocedal · 1989
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Training Products of Experts by Minimizing Contrastive Divergence
Geoffrey E. Hinton · 2002
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Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen and Peter Dayan · 2005
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Hopfield neural networks: A survey
Humayun Karim Sulehria and Ye Zhang · 2007
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Least Squares Estimation Without Priors or Supervision
Martin Raphan and Eero Simoncelli · 2011
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A Connection Between Score Matching and Denoising Autoencoders
Pascal Vincent · 2011
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A Study on Associative Neural Memories
B. D. C. N. Prasad, P. E. S. N. Krishna Prasad, Sagar Yeruva, and P. Sita Rama Murty · 2012
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Auto-Encoding Variational Bayes
Diederik P. Kingma and Max Welling · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Deep Unsupervised Learning using Nonequilibrium Thermodynamics, November 2015
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Memory Networks, November 2015
Jason Weston, Sumit Chopra, and Antoine Bordes · 2015
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Dense associative memory for pattern recognition
Dmitry Krotov and John J. Hopfield · 2016
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Key-Value Memory Networks for Directly Reading Documents, October 2016
Alexander Miller, Adam Fisch, Jesse Dodge, Amir-Hossein Karimi, Antoine Bordes, and Jason Weston · 2016
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Neural distributed autoassociative memories: A survey
V. I. Gritsenko, Dmitri A. Rachkovskij, A. A. Frolov, Ross W. Gayler, Denis Kleyko, and Evgeny Osipov · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz 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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Adam: A Method for Stochastic Optimization, January 2017
Diederik P. Kingma and Jimmy Ba · 2017
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FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models
Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud · 2018
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Survey of Content Addressable Memory
Sivakumar S.A, A. Swedha, and Dr Naveen · 2018
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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Generative Modeling by Estimating Gradients of the Data Distribution
Yang Song and Stefano Ermon · 2019
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Improved Techniques for Training Score-Based Generative Models
Yang Song and Stefano Ermon · 2020
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Denoising Diffusion Probabilistic Models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Score-Based Generative Modeling through Stochastic Differential Equations
Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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A Pedagogical Introduction to Score Models, April 2022
Eric J. Ma · 2022
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Understanding Diffusion Models: A Unified Perspective, August 2022
Calvin Luo · 2022
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Hopfield Networks is All You Need
Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich, Lukas Gruber, Markus Holzleitner, Thomas Adler, David Kreil, Michael K. Kopp, Günter Klambauer, Johannes Brandstetter, and Sepp Hochreiter · 2022
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Elucidating the Design Space of Diffusion-Based Generative Models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
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Content addressable memory without catastrophic forgetting by heteroassociation with a fixed scaffold
Sugandha Sharma, Sarthak Chandra, and Ila Fiete · 2022
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A Universal Abstraction for Hierarchical Hopfield Networks
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Sliced score matching: A scalable approach to density and score estimation
Yang Song, Sahaj Garg, Jiaxin Shi, and Stefano Ermon · 2020
Cited alongside, same era.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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 · 2020
Cited alongside, same era.
Modern Hopfield Networks and Attention for Immune Repertoire Classification
Michael Widrich, Bernhard Schäfl, Milena Pavlović, Hubert Ramsauer, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter, Geir Kjetil Sandve, Victor Greiff, Sepp Hochreiter, and Günter Klambauer · 2020
Cited alongside, same era.
Scaling Laws for Neural Language Models, January 2020
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
Cited alongside, same era.
Diffusion Models Beat GANs on Image Synthesis
Prafulla Dhariwal and Alexander Quinn Nichol · 2021
Cited alongside, same era.
Large associative memory problem in neurobiology and machine learning
Dmitry Krotov and John J. Hopfield · 2021
Cited alongside, same era.
Benjamin Hoover, Duen Horng Chau, Hendrik Strobelt, and Dmitry Krotov · 2022
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Score-based Denoising Diffusion with Non-Isotropic Gaussian Noise Models, November 2022
Vikram Voleti, Christopher Pal, and Adam Oberman · 2022
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Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise, August 2022
Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie S. Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2022
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All are Worth Words: A ViT Backbone for Score-based Diffusion Models
Fan Bao, Chongxuan Li, Yue Cao, and Jun Zhu · 2022
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Your ViT is Secretly a Hybrid Discriminative-Generative Diffusion Model, August 2022
Xiulong Yang, Sheng-Min Shih, Yinlin Fu, Xiaoting Zhao, and Shihao Ji · 2022
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Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models
Beren Millidge, Tommaso Salvatori, Yuhang Song, Thomas Lukasiewicz, and Rafal Bogacz · 2022
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On the Mathematics of Diffusion Models, February 2023
David McAllester · 2023
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High-resolution image reconstruction with latent diffusion models from human brain activity
Yu Takagi and Shinji Nishimoto · 2023
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Pattern completion and disruption characterize contextual modulation in mouse visual cortex, March 2023
Jiakun Fu, Suhas Shrinivasan, Kayla Ponder, Taliah Muhammad, Zhuokun Ding, Eric Wang, Zhiwei Ding, Dat T. Tran, Paul G. Fahey, Stelios Papadopoulos, Saumil Patel, Jacob Reimer, Alexander S. Ecker, Xaq Pitkow, Ralf M. Haefner, Fabian H. Sinz, Katrin Franke, and Andreas S. Tolias · 2023
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Brain-Diffuser: Natural scene reconstruction from fMRI signals using generative latent diffusion, March 2023
Furkan Ozcelik and Rufin VanRullen · 2023
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Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?
Arjun Majumdar, Karmesh Yadav, Sergio Arnaud, Yecheng Jason Ma, Claire Chen, Sneha Silwal, Aryan Jain, Vincent-Pierre Berges, Pieter Abbeel, Dhruv Batra, Yixin Lin, Oleksandr Maksymets, Aravind Rajeswaran, and Franziska Meier · 2023
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Building transformers from neurons and astrocytes
Leo Kozachkov, Ksenia V. Kastanenka, and Dmitry Krotov · 2023
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What’s the score? – Review of latest Score Based Generative Modeling papers., 2023
James Thorton and De Bortoli · 2023
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A new frontier for Hopfield networks
Dmitry Krotov · 2023
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In search of dispersed memories: Generative diffusion models are associative memory networks, September 2023
Luca Ambrogioni · 2023
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Energy Transformer, February 2023
Benjamin Hoover, Yuchen Liang, Bao Pham, Rameswar Panda, Hendrik Strobelt, Duen Horng Chau, Mohammed J. Zaki, and Dmitry Krotov · 2023
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Consistency Models
Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
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Extracting training data from diffusion models
Nicolas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramer, Borja Balle, Daphne Ippolito, and Eric Wallace · 2023
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Simplicial hopfield networks
Thomas F Burns and Tomoki Fukai · 2023
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Scaling Laws for Associative Memories, October 2023
Vivien Cabannes, Elvis Dohmatob, and Alberto Bietti · 2023
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Spontaneous symmetry breaking in generative diffusion models
Gabriel Raya and Luca Ambrogioni · 2024
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Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory, May 2024
Xueyan Niu, Bo Bai, Lei Deng, and Wei Han · 2024
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