[D] Hash table aspects of ReLU neural networks

Reddit r/MachineLearning / 4/5/2026

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The article reframes a ReLU layer by expressing its gating as a diagonal 0/1 matrix so that the layer output can be written in the form DWx.
It investigates how the next-layer computation Wₙ₊₁Dₙ can be interpreted as an effective linear mapping selected by the ReLU decisions.
One proposed interpretation is that this structure behaves like a locality-sensitive hash table lookup over the space of linear mappings.
Another view treats the mechanism as an associative memory, where the diagonal matrix Dₙ functions as a key that gates or retrieves information.
It notes that these perspectives are still preliminary, with unresolved integration of viewpoints and some notation issues, though the underlying concepts are presented as simple.

If you collect the ReLU decisions into a diagonal matrix with 0 or 1 entries then a ReLU layer is DWx, where W is the weight matrix and x the input.

What then is Wₙ₊₁Dₙ where Wₙ₊₁ is the matrix of weights for the next layer?

It can be seen as a (locality sensitive) hash table lookup of a linear mapping (effective matrix). It can also be seen as an associative memory in itself with Dₙ as the key.

There is a discussion here:

https://discourse.numenta.org/t/gated-linear-associative-memory/12300

The viewpoints are not fully integrated yet and there are notation problems.

Nevertheless the concepts are very simple and you could hope that people can follow along without difficulty, despite the arguments being in such a preliminary state.

submitted by /u/oatmealcraving
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[D] Hash table aspects of ReLU neural networks

Key Points

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