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Modern Hopfield networks have enjoyed recent interest due to their connection to attention in transformers.
Resultats sur l’empilement de calottes egales sur une perisphere de ℝ n \mathbb{R}^{n} et correction a un travail anterieur
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Neural associative memories and sparse coding
Palm, G · 2013
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CA3 retrieves coherent representations from degraded input: direct evidence for CA3 pattern completion and dentate gyrus pattern separation
Neunuebel, J. and Knierim, J · 2014
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Dense associative memory for pattern recognition
Krotov, D. and Hopfield, J. J · 2016
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Rationalizing neural predictions
Lei, T., Barzilay, R., and Jaakkola, T · 2016
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Interpretable neural predictions with differentiable binary variables
Bastings, J., Aziz, W., and Titov, I · 2019
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Adaptively sparse transformers
Correia, G. M., Niculae, V., and Martins, A. F · 2019
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Sparse sequence-to-sequence models
Peters, B., Niculae, V., and Martins, A. F · 2019
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Learning with Fenchel-Young losses
Blondel, M., Martins, A. F., and Niculae, V · 2020
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An exponential learning rate schedule for deep learning
Li, Z. and Arora, S · 2020
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Explain and predict, and then predict again
Zhang, Z., Rudra, K., and Anand, A · 2020
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From softmax to sparsemax: A sparse model of attention and multi-label classification
Martins, A. and Astudillo, R · 2016
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Optnet: Differentiable optimization as a layer in neural networks
Amos, B. and Kolter, J. Z · 2017
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On a model of associative memory with huge storage capacity
Demircigil, M., Heusel, J., Löwe, M., Upgang, S., and Vermet, F · 2017
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
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A regularized framework for sparse and structured neural attention
Niculae, V. and Blondel, M · 2017
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A combinatorial model for dentate gyrus sparse coding
Severa, W., Parekh, O., James, C., and Aimone, J · 2017
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Guerreiro, N. M. and Martins, A. F. T · 2021
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Aligning faithful interpretations with their social attribution
Jacovi, A. and Goldberg, Y · 2021
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Hopfield networks is all you need
Ramsauer, H., Schäfl, B., Lehner, J., Seidl, P., Widrich, M., Gruber, L., Holzleitner, M., Adler, T., Kreil, D., Kopp, M. K., Klambauer, G., Brandstetter, J., and Hochreiter, S · 2021
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Biological learning in key-value memory networks
Tyulmankov, D., Fang, C., Vadaparty, A., and Yang, G. R · 2021
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Relating transformers to models and neural representations of the hippocampal formation
Whittington, J. C., Warren, J., and Behrens, T. E · 2021
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Universal hopfield networks: A general framework for single-shot associative memory models
Millidge, B., Salvatori, T., Song, Y., Lukasiewicz, T., and Bogacz, R · 2022
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Retrieving k k -nearest memories with modern Hopfield networks
Davydov, A., Jaffe, S., Singh, A., and Bullo, F · 2023
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Energy transformer
Hoover, B., Liang, Y., Pham, B., Panda, R., Strobelt, H., Chau, D. H., Zaki, M. J., and Krotov, D · 2023
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On sparse modern hopfield model
Hu, J. Y.-C., Yang, D., Wu, D., Xu, C., Chen, B.-Y., and Liu, H · 2023
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STanhop: Sparse tandem hopfield model for memory-enhanced time series prediction
Wu, D., Hu, J. Y.-C., Li, W., Chen, B.-Y., and Liu, H · 2024
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