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From: Mild Shock <janburse@fastmail.fm>
Newsgroups: comp.lang.python
Subject: Re: ANN: Dogelog Player 1.2.6 (Segmented Fileaccess)
Date: Thu, 13 Feb 2025 19:12:22 +0100
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Xref: csiph.com comp.lang.python:197245


An autoencoder learns two functions: an encoding
function that transforms the input data, and a
decoding function that recreates the input data
from the encoded representation. We approach
autoencoders via our already developed SAT Learning
in the Prolog programming language.

Switching from a marginal maximizer to a conditional
maximizer gives better results but also requires a
more costly and slower optimizer. Maximum entropy
methods were already suggest by Peter Cheeseman in
1987. Mostlikely flawed since there is not yet a
feedback loop from the decoder to the encoder.

Maximum Entropy in SAT Autoencoding
https://x.com/dogelogch/status/1890093860782764409

Maximum Entropy in SAT Autoencoding
https://www.facebook.com/groups/dogelog

Mild Shock schrieb:
> Dogelog Player is a Prolog system for JavaScript,
> Python and Java. It is 100% written in Prolog itself.
> We present an enhancement to DCG translation. It uses
> unification spilling to reduce the number of needed
> unify (=)/2 calls and intermediate variables.
> 
> Unification spilling can be readily implemented by
> performing unification (=)/2 during DCG translation.
> Careful spilling without breaking steadfastness gave
> us a 10% — 25% speed increase not only for the calculator
> example but also for the Albufeira transpiler.
> 
> See also:
> 
> DCG Translation with Unification Spilling
> https://x.com/dogelogch/status/1889270444647182542
> 
> DCG Translation with Unification Spilling
> https://www.facebook.com/groups/dogelog
> 
>