1) What is attention mechanism (since it was mentionned in my resume) 2) What is the sde of the Heston model? 3) What is implied volatility? 4) What is PCA (machine learning question)? 5) How matrices are inversed in a computer program?

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Accepted Answer

Answers to various quantitative research questions

Attention mechanism is a key component in deep learning models that allows the model to focus on specific parts of the input sequence.
SDE stands for Stochastic Differential Equation in the context of the Heston model used in quantitative finance.
Implied volatility is the market's expectation of future volatility implied by the prices of options.
PCA (Principal Component Analysis) is a technique used for dimensionality reduction in machine learning by finding the principal components of the data.
Matrices are inversed in a computer program using algorithms like Gaussian elimination or LU decomposition.

Accepted Answer

1) Explain how embedding are produced from tokens to attention scores....
2) It is a stochastic volatility model, but I didn't know the sde at that time
3) The sigma that makes the model price of an asset equal to the market price (not sure)
4) Dimentionality reduction technique that uses the most relevant eigen vectors of covariance matrix...
5)  LU decomposition