screwlispMar 19

https://lispy-gopher-show.itch.io/dl-roc-lisp/devlog/1463131/game-emeddable-small-deep-learning-assets-are-a-receiver-operating-characteristic-part-0

My grandiosity crept in while I was trying to convey that I am pretty sure I am pretty onto something.

Anyway, this is part 0 of my #itchio series on my #deepLearning by receiver operating characteristic #statistics #dl #roc #DLROC implemented in pure ansi #commonLisp based on the condition system.

Time 0:
> Sharks.
Time 1:
> Feed sharks humans.
Time 2:
> Feed sharks.

It is very enlightening for me personally.

It is a bit early, but I hope *you* participate with it.

Game-Emeddable small Deep Learning (-assets) are a Receiver Operating Characteristic Part 0 - DL-ROC.LISP by screwtape

To say the ending first, look at this tiny deep learning inference, where I have embedded symbols in literally the same gold standard (“training data”) of my early article https://screwlisp.small-...

itch.io
Show threadThunaMar 19

@screwlisp I've been trying to make sense of the code, but I'm not managing it. Could you explain what code you ran to get the "Time 0-1-2" output you're showing?

Am I correct in my understanding that for SUM and CONC branches to be meaningful you need to define the corresponding handlers in DL-ROC? As it stands they don't seem to do anything.

Show threadscrewlispMar 19

@thuna yes, it is a framework using the condition system 'protocol'. SUM, CONC, COLLECT (and MSCP) are all just local restarts that are potentially available to a COMPARISON signal, and they were chosen to correspond to loop's :sum, :nconc and :collect . (and just :do, for mscp which probably should also have had a restart).

I can explain the time-0-1-2 code from yet-unpublished-article. The data and starting context are the ones used. feed, sharks and humans are feature neurons read by a fun.

Show threadscrewlispMar 19

@thuna So in order to get something collected by cl:loop, you would

(defun collect-this (c &aux (r (find-restart 'collect c)))
(when r (invoke-restart r 'thing-to-collect)))

(handler-bind ((comparison #'collect-this))
...)

Show threadscrewlispMar 19

@thuna 3/1
The article is scheduled for part-2 -being-a-more-formal-writeup.

My ROC update rule equation adapted from Krotov and Hopfield 2016 for time n+1 is a receiver operating characteristic of the neurons at timestep n, which I initially wrote to show that a grid of sensors whose output is an instruction to feed sharks or feed humans (at my recurring theme of a shark aquarium restaurant) can have training data trivially added to make the hallucination at time step 1 of Feed sharks humans.

Show threadscrewlispMar 19
@thuna I could do with some scrutiny. Try writing
Χᵩᵢ = TPᵩⱼ + TNᵩⱼ − FPᵩⱼ − FNᵩⱼ, 𝑗≠𝑖
Yᵩᵢ = P( Χᵩᵢ + ξᵩᵢ ) − P( Χᵩᵢ − ξᵩᵢ )
I am calling Y a receiver operating characteristic because X is X(TP, TN, FP, FN).
Vᵢ⁽ⁿ⁺¹⁾ = -1 if (minusp ∑ᵩYᵩᵢ) else +1.
adapted from:
Krotov and Hopfield 2016
Vᵢ⁽ⁿ⁺¹⁾=sign[∑ᵩ(F(+ξᵩᵢ+∑ⱼξᵩⱼVⱼ⁽ⁿ⁾)-F(-ξᵩᵢ+∑ⱼξᵩⱼVⱼ⁽ⁿ⁾))], 𝑗≠𝑖, 𝜑 indexing into the memories, neurons V at timesteps 𝑛 and 𝑛+1. F, a rectified polynomial.
Show threadThunaMar 19
@screwlisp I am currently trying to rewrite your code in the CLOS framework, to see if I can more easily follow that, but afterwards I do want to see what the Krotov and Hopfield 2016 paper says to try and square that up against your writeup.
Show threadThuna

@screwlisp So, one thing I'm confused about; why does the first row of CONTEXT look like that? At what point are the second items in e.g. (0 FEED) used?

Also, I don't understand what the point of splitting CONTEXT and GOLD-STANDARD into rows is, since you are just summing/collecting/concatenating over every item.

Mar 19 at 2:05pmWeb
Show threadscrewlispMar 19

@thuna on the first topic, because my neurons are conses with a predicate on a cons.

Most of your questions are covered in an article I wrote first, but did not publish first since I worried it came out too strongly on implementation details and unqualified equations so I moved it to part 2, though I will release it later today after some appointments.

I think emphasising multispectrality is important, and jaggedness being possible. Bands of an audio chirp? Spatial 2D mask?