What results does your compressor have on some benchmarks?

On Tue, Nov 19, 2019, 4:19 PM <[email protected]> wrote:

> On Tuesday, November 19, 2019, at 11:42 AM, Matt Mahoney wrote:
>
> The best compressors are very complex. They use hundreds or thousands of
> independent context models and adaptively combine their bit predictions and
> encodes the prediction error. The decompressor uses an exact copy of the
> model trained on previous output to reconstruct the original data.
>
>
> THAT Doesn't sound very complex :) You literally just told us it: *combines
> models into 1 model & adaptively predicts next bit.*
>
> Can you add "*details*" to that? :)
>
> *My understanding* is it does Huffman coding to eliminate *totally
> useless* up-scaling ignorant humans subjected it to. Then it combines
> many randomly-initiated web heterarchies like w2v/seq2seq of
> word-part/word/phrase code meanings, and combines many randomly-initiated
> models of the text for entailment purposes that used modern Transformer
> BERT Attention to know next word-part/word/phrase candidates plus frequency
> based on prior words and related meaning words from heterarchy. When it
> predicts the next bit it basically knows what word-parts/words/phrases are
> around (ya, bi-direction BERT tech) it including related meanings and based
> on frequency and scores it will decide the range candidate.
>
> *Why does it work?* Because patterns are in words and word parts and
> phrases, like 7zip recognizes. There's frequency as well. *When the next
> bit or bits* are predicted it knows what candidates there is to place
> next (or to refine one already added) and it *looks at codes around it*
> for context and sees multiple bit codes around it like boy/girl or ism/ing
> and knows the frequency of these codes and of what entails them and pays
> attention to related meanings around it as well to add to the score. Repeat
> recursively bi-directionally.
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