High order correlation model for associative memory

By H. H. Chen, Y. C. Lee, G. Sun, H. Y. Lee, T. Maxwell, C. Lee Giles. 1987

Subfields: Model Architectures

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Model Architectures

1. Günter Klambauer
2. Dmitry Krotov
3. Sepp Hochreiter
4. Hubert Ramsauer
5. Angela Bitto-Nemling
6. Markus Holzleitner
7. John J. Hopfield
8. J. Urgen Schmidhuber

Citations in the corpus

• CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP (2021, background): “Their storage capacity can be considerably increased by polynomial terms in the energy function (Chen et al., 1986; Psaltis & Cheol, 1986; Baldi & Venkatesh, 1987; Gardner, 1987; Abbott & Arian, 1987; Horn & Usher, 1988; Caputo & Niemann, 2002; Krotov & Hopfield, 2016).”
• Dense Associative Memory for Pattern Recognition (2016, methodology): “For the case of polynomial energy function a very similar family of models was considered in [11, 12, 13, 14, 15, 16].”
• History Compression via Language Models in Reinforcement Learning (2022, background): “Their storage capacity can be considerably increased by polynomial terms in the energy function (Chen et al., 1987; Psaltis & Park, 1986; Baldi & Venkatesh, 1987; Gardner, 1987; Abbott & Arian, 1987; Horn & Usher, 1988; Caputo & Niemann, 2002; Krotov & Hopfield, 2016).”