AAAAA I want a good backpack.

requirements

1. explicit laptop compartment (whether intentional or not; water bladder component that fits a laptop is fine)
2. earbags (those fannypack things on the side of the bottom belt); needs to be large (i.e. enough to fit an iphone 5)
3. raincover
4. at least 3 compartments, ideally one with a pen holder and key ring, and the outermost being very accessible (think mesh bag)

basically I want an exact replica of the columbia silver ridge 30L from 2012 which they don’t sell anymore; the new one breaks 4) slightly and is also $150 and I got mine for ilke $60-70 (it was like 300-350 rmb) max in 2012

we need to keep two sequences aligned; so in addition to minimum edit distance we need to know how to transform one sequence into another.

To do this, we keep a pointer of what cell we came from.

This is similar to edit distance with DP, but we keep a pointer of each cell of the action: point DOWN (less j) if inserting, point LEFT (less i) if deleting, and point diagonally if substituting.

Bag of Words is an order-free representation of a corpus. Specifically, each word has a count which we assign to each word without any other information about ordering, etc.

With the Bayes Theorem, we have:

\begin{equation} C_{MAP} = \arg\max_{c \in C} P(d|c)P( c) \end{equation}

where \(d\) is the document, and \(c\) is the class.

So, given a document is a set of a bunch of words:

\begin{equation} C_{MAP} = \arg\max_{c\in C} P(x_1, \dots, x_{n}|c)P( c) \end{equation}

DOI: 10.3389/fnagi.2021.635945

One-Liner

extracted lexicographic and syntactical features from ADReSS Challenge data and trained it on various models, with BERT performing the best.

Novelty

???????

Seems like results here are a strict subset of Zhu 2021. Same sets of dataprep of Antonsson 2021 but trained on a BERT now. Seem to do worse than Antonsson 2021 too.

Notable Methods

Essentially Antonsson 2021

• Also performed MMSE score regression.

Key Figs

Table 7 training result

This figure shows us that the results attained by training on extracted feature is past the state-of-the-art at the time.

Random thoughts as I scan through the book:

Central framing: learning as a means to generalize + predict

Key Tasks

• regression (predict a value)
• binary classification (sort an example into a boolean class of Y/N)
• multi-class classification (sort an example into multiple classes)
• ranking (sorting an object into relevant order)

Optimality of Bayes Optimal Classifier

If you have the underlying data distribution, we can classify \(y\) from \(x\) by choosing: