# Tag Info

Accepted

### Why has statistics-based AI become more popular than other forms of AI?

The availability of large data sets. In symbolic/rule-based AI, the 'knowledge' has to be hand-coded, usually by experts. This is expensive and limited to small-scale problems only. In statistical AI/...

### How much statistics is involved in AI?

Many people without a formal/solid background in statistics (e.g. without knowing exactly what the central limit theorem (CLT) states) are doing research on machine learning, which is a very big and ...
Accepted

### Why is Standard Deviation based on L2 Variance and not L1 Variance

I've found the answer, the L2 is Standard Deviation, and the L1 is Mean Deviation. Standard deviation describes the variation better and the values are always different on different sets of X while ...
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### What does it mean when a model "statistically outperforms" another?

Most model-fitting is stochastic, so you get different parameters every time you train, and you usually can't say that one algorithm will always give you a better-performing model. However, since you ...

### What is the difference between model and data distributions?

Yes. In Machine Learning we consider that the samples in your training set are sampled from an underlying distribution called the data generating distribution. Generative models classify the samples ...
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### How does $\mathbb{E}$ suddenly change to $\mathbb{E}_{\pi'}$ in this equation?

Also, in general, in the conditional expectation, which distribution do we compute the expectation with respect to? From what I have seen, in $\mathbb{E}[X|Y]$, we always calculate the expected value ...

### What is Bayes' theorem?

Bayes' theorem relates conditional probabilities: $$P(A \mid B) = \frac{P(B \mid A) P(A)}{P(B)}$$

### How much statistics is involved in AI?

I work in NLP, and use very little statistics. Actually, almost nothing I do can be classed as 'serious' statistics. So yes, AI is a wide area, and in my company there is a group that does machine ...
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### Research paths/areas for improving the performance of CNNs when faced with limited data

Some research areas that come to mind which can be useful when faced with a limited amount of data: Regularization: Comprises different methods to prevent the network from overfitting, to make it ...
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### Which generalization of standard deviation to use for multidimensional input normalization

This idea is sometimes applied in computer vision, under the name of Whitening Transform, or ZCA sphering transform. The name whitening comes from signal processing, since removing correlation from a ...

### Coherence is classifying time series data

The following paper from Amazon Alexa Research refers to this topic as keyword spotting (KWS), and more specifically, to wake up words as wake word (WW) spotting. Jose, C., Mishchenko, Y., Senechal, ...
1 vote

### What is wrong in reasoning here in classification for defect detection?

Alpha represents the significance level, or the probability that you will make a Type I error by rejecting the null hypothesis when it is actually true - in other words, the probability that you're ...
1 vote
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### Markov's Decision Process - calculate value in each iteration

The value iteration algorithm defines the following update rule (reference is slide 11 in this MIT course): $$V_{i+1}(s) = \max_{a}\{R(s,a) + \gamma E_{s'\sim T(.|s,a)} V_i(s')\}$$ for all states $s$, ...
1 vote

### What can be an example other than batch normalization that uses statistics of batches?

Here's some examples: Group Normalization Layer Normalization Switchable Normalization Attentive Normalization Spectrl Normalization Notice how in general different normalization techniques are ...
1 vote

### What is the definition of "confidence interval" around a (complicated) function?

Off the top of my head, I don't know the very specific definition of confidence interval (or whether it's only defined for the parameters of a model), as I am not a statistician. In any case, ...
1 vote
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### What would be the reason behind using plots (such as box-plots or histograms) for ML development?

At a basic level, these kinds of low-dimensional plots where you look at one or two variables at a time can help to give you a sense of what types of relationships you might expect to see, such as ...
1 vote

### Can AI be understood as a generalized statistics tool?

My understanding is that AI can be understood as a very generalized and abstract statistics software package handling input data in a general way to find the "best fit" to some form of ...
1 vote
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### Why is probability that at least one hypothesis out of $k$ being consistent with $m$ training examples $k(1- \epsilon)^m$?

Let $A$ and $B$ be two events. In general, the probability that either $A$ or $B$ occurs is defined as $$P(A \text{ or } B) = P(A) + P(B) - P(A \text{ and } B)$$ If $A$ and $B$ are disjoint, i.e. ...

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