Decentralized training seems to improve sample complexity for

DOI: 10.3389/fnagi.2021.642647

One-Liner

Combined bag-of-words on transcript + ADR on audio to various classifiers for AD; ablated BERT’s decesion space for attention to make more easy models in the future.

Novelty

• Pre-processed each of the two modalities before fusing it (late fusion)
• Archieved \(93.75\%\) accuracy on AD detection
• The data being forced-aligned and fed with late fusion allows one to see what sounds/words the BERT model was focusing on by just focusing on the attention on the words

Notable Methods

• Used classic cookie theft data
• bag of words to do ADR but for words
• multimodality but late fusion with one (hot-swappable) classifier

Key Figs

How they did it

This is how the combined the forced aligned (:tada:) audio and transcript together.

The Martingale Model states: if we observed the closing price of the market yesterday, we expect that the market is going to open at the close price yesterday.

Formally:

\begin{equation} E\qty [X_{k}|X_{k-1}, X_{k-2},\ldots] = X_{k-1} \end{equation}

“irrespective of what you know, no matter how long the history, the best expectation of today’s price is yesterday’s price.”

This is not a for sure! modeling statement: this is simply the expected value!! That means, after \(\infty\) times of re-running the universe starting “yesterday”, the new opening price will converge to the last closing price.