After a long digression, I'm finally back to one of the main lines of research that I wanted to write about. The two main ideas in this post are not that recent but have been quite impactful (one of the papers won a recent ICML test of time award). They address two of the topics that are near and dear to my heart: Bayesian learning and scalability. Dare I even ask who wouldn't be interested in the intersection of these topics?

This post is about two techniques to perform scalable Bayesian inference. They both address the problem using stochastic gradient descent (SGD) but in very different ways. One leverages the observation that SGD plus some noise will converge to Bayesian posterior sampling [Welling2011], while the other generalizes the "reparameterization trick" from variational autoencoders to enable non-Gaussian posterior approximations [Blundell2015]. Both are easily implemented in the modern deep learning toolkit thus benefit from the massive scalability of that toolchain. As usual, I will go over the necessary background (or refer you to my previous posts), intuition, some math, and a couple of toy examples that I implemented.

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Through a couple of different avenues I wandered, yet again, down a rabbit hole leading to the topic of this post. The first avenue was through my main focus on a particular machine learning topic that utilized some concepts from physics, which naturally led me to stochastic calculus. The second avenue was through some projects at work in the quantitative finance space, which is one of the main applications of stochastic calculus. Naively, I thought I could write a brief post on it that would satisfy my curiosity -- that didn't work out at all! The result is this extra long post.

This post is about stochastic calculus, an extension of regular calculus to stochastic processes. It's not immediately obvious but the rigour needed to properly understand some of the key ideas requires going back to the measure theoretic definition of probability theory, so that's where I start in the background. From there I quickly move on to stochastic processes, the Wiener process, a particular flavour of stochastic calculus called Itô calculus, and finally end with a couple of applications. As usual, I try to include a mix of intuition, rigour where it helps intuition, and some simple examples. It's a deep and wide topic so I hope you enjoy my digest of it.

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This post has been a long time coming. I originally started working on it several posts back but hit a roadblock in the implementation and then got distracted with some other ideas, which took me down various rabbit holes (here, here, and here). It feels good to finally get back on track to some core ML topics. The other nice thing about not being an academic researcher (not that I'm really researching anything here) is that there is no pressure to do anything! If it's just for fun, you can take your time with a topic, veer off track, and the come back to it later. It's nice having the freedom to do what you want (this applies to more than just learning about ML too)!

This post is going to talk about a class of deep probabilistic generative models called normalizing flows. Alongside Variational Autoencoders and autoregressive models 1 (e.g. Pixel CNN and Autoregressive autoencoders), normalizing flows have been one of the big ideas in deep probabilistic generative models (I don't count GANs because they are not quite probabilistic). Specifically, I'll be presenting one of the earlier normalizing flow techniques named Real NVP (circa 2016). The formulation is simple but surprisingly effective, which makes it a good candidate to understand more about normalizing flows. As usual, I'll go over some background, the method, an implementation (with commentary on the details), and some experimental results. Let's get into the flow!

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Here's a topic I thought that I would never get around to learning because it was "too hard". When I first started learning about Bayesian methods, I knew enough that I should learn a thing or two about MCMC since that's the backbone of most Bayesian analysis; so I learned something about it (see my previous post). But I didn't dare attempt to learn about the infamous Hamiltonian Monte Carlo (HMC). Even though it is among the standard algorithms used in Bayesian inference, it always seemed too daunting because it required "advanced physics" to understand. As usual, things only seem hard because you don't know them yet. After having some time to digest MCMC methods, getting comfortable learning more maths (see here, here, and here), all of a sudden learning "advanced physics" didn't seem so tough (but there sure was a lot of background needed)!

This post is the culmination of many different rabbit holes (many much deeper than I needed to go) where I'm going to attempt to explain HMC in simple and intuitive terms to a satisfactory degree (that's the tag line of this blog after all). I'm going to begin by briefly motivating the topic by reviewing MCMC and the Metropolis-Hastings algorithm then move on to explaining Hamiltonian dynamics (i.e., the "advanced physics"), and finally discuss the HMC algorithm along with some toy experiments I put together. Most of the material is based on [1] and [2], which I've found to be great sources for their respective areas.

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A lot of modern machine learning is related to this idea of "compression", or maybe to use a fancier term "representations". Taking a huge dimensional space (e.g. images of 256 x 256 x 3 pixels = 196608 dimensions) and somehow compressing it into a 1000 or so dimensional representation seems like pretty good compression to me! Unfortunately, it's not a lossless compression (or representation). Somehow though, it seems intuitive that there must be a way to use what is learned in these powerful lossy representations to help us better perform lossless compression, right? Of course there is! (It would be too anti-climatic of a setup otherwise.)

This post is going to introduce a method to perform lossless compression that leverages the learned "compression" of a machine learning latent variable model using the Bits-Back coding algorithm. Depending on how you first think about it, this seems like it should either be (a) really easy or (b) not possible at all. The reality is kind of in between with an elegant theoretical algorithm that is brought down by the realities of discretization and imperfect learning by the model. In today's post, I'll skim over some preliminaries (mostly referring you to previous posts), go over the main Bits-Back coding algorithm in detail, and discuss some of the implementation details and experiments that I did while trying to write a toy version of the algorithm.

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