As I am writing this blogpost, MIT is about to close until next fall and the undergrads are essentially getting evicted from the dorms.
Last two days were some of the most stressful days I have ever had. I have tons of work to do, but I cannot concentrate because I need to make sure my visa status will be okay, I need to pack my belongings and hopefully I’ll find a place to go.
COVID-19 is spreading rapidly across the US. This was expected. While MIT’s take on this is understandable, was it really necessary?
Let me try to answer that.
Let’s start with a small campus with \(N\) students where a small number of students \(\eta\) have been tested COVID-19 positive. I’ll also assume that the campus is in a state of lockdown — no student leaves the campus and no person from outside enters the campus. Continue reading A Probabilistic Model for COVID-19 Outbreak→
Variational Autoencoders (VAE) are really cool machine learning models that can generate new data. It means a VAE trained on thousands of human faces can new human faces as shown above!
Recently, two types of generative models have been popular in the machine learning community, namely, Generative Adversarial Networks (GAN) and VAEs. While GANs have had more success so far, a recent Deepmind paper showed that VAEs can yield results competitive to state of the art GAN models. Furthermore, VAE generated images retain more of the diversity of training dataset than GAN counterparts. Continue reading An Introduction to Variational Autoencoders→
Let’s have a look at this god-tier math puzzle. Only one out of seven gets it right, and the other six don’t. So, what are the odds for solving it correctly? 1 to 6. Generally, if \(p\) is the probability that someone will get it right, then his/her odds are \(p/(1-p)\).
However, it isn’t necessarily true that \(p=1/7\) for every person because some people are smarter, some have better education and so on. Hence, \(p\) also depends on the person attempting the puzzle. In a Bayesian framework, we capture this dependence with conditional probability.
My mistress’ eyes are nothing like the sun;
Coral is far more red than her lips’ red;
If snow be white, why then her breasts are dun;
If hairs be wires, black wires grow on her head.
I have seen roses damask’d, red and white,
But no such roses see I in her cheeks; Continue reading A Week of Poetry: Day 3→
A graph \(G(V, E)\) is called bipartite if its vertices can be divided into two groups \(X\) and \(Y\) such that every edge connects one vertex from \(X\) and one vertex from \(Y\). The graph drawn below is bipartite.
This is a very unusual post for this blog. I hope my regular readers will bear with me.
I love Game of Thrones. I have read the books and the companion novels. I have been following the series for years. I am familiar with most of the fan theories too. Tonight the series finale aired. I didn’t like how the show ended. It felt rushed and very baffling. Continue reading How Game of Thrones Should Have Ended→