Storage capacity of generalized networks.

By L. F. Abbott, Yair Arian. 1987

Subfields: Statistical Learning Theory

How researchers see this paper

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

In Statistical Learning Theory

1. Hubert Ramsauer
2. Günter Klambauer
3. Angela Bitto-Nemling
4. Dmitry Krotov
5. John J. Hopfield
6. Sepp Hochreiter

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 higher powers n the capacity rapidly grows with N in a non-linear way, allowing the network to store and reliably retrieve many more patterns than the number of neurons that it has, in accord1 with [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).”