Inventors of the original artificial neural networks (NNs) derived their inspiration from biology. However, as artificial NNs progressed, their design was less guided by neuroscience facts. Meanwhile, progress in neuroscience has altered our conceptual understanding... Continue
From text translation to video captioning, learning to map one sequence to another is an increasingly active research area in machine learning. Fueled by the success of recurrent neural networks in its many variants, the... Continue
Word embeddings capture the meaning of a word using a low-dimensional vector and are ubiquitous in natural language processing (NLP). (See my earlier post 1 and post2.) It has always been unclear how to interpret... Continue
Previously Rong’s post and Ben’s post show that (noisy) gradient descent can converge to local minimum of a non-convex function, and in (large) polynomial time (Ge et al.’15). This post describes a simple framework that... Continue
In this post, we will see the main technical ideas in the analysis of the mixing time of evolutionary Markov chains introduced in a previous post. We start by introducing the notion of the expected... Continue
Thanks to Rong for the very nice blog post describing critical points of nonconvex functions and how to avoid them. I’d like to follow up on his post to highlight a fact that is not... Continue
Convex functions are simple — they usually have only one local minimum. Non-convex functions can be much more complicated. In this post we will discuss various types of critical points that you might encounter when... Continue
Central to machine learning is our ability to relate how a learning algorithm fares on a sample to its performance on unseen instances. This is called generalization. In this post, I will describe a purely... Continue
In this post we present a high level introduction to evolution and to how we can use mathematical tools such as dynamical systems and Markov chains to model it. Questions about evolution then translate to... Continue
This is a followup to an earlier post about word embeddings, which capture the meaning of a word using a low-dimensional vector, and are ubiquitous in natural language processing. I will talk about my joint... Continue