The previous post described Generative Adversarial Networks (GANs), a technique for training generative models for image distributions (and other complicated distributions) via a 2-party game between a generator deep net and a discriminator deep net.... Continue
Since ability to generate “realistic-looking” data may be a step towards understanding its structure and exploiting it, generative models are an important component of unsupervised learning, which has been a frequent theme on this blog.... Continue
Given the sheer number of backpropagation tutorials on the internet, is there really need for another? One of us (Sanjeev) recently taught backpropagation in undergrad AI and couldn’t find any account he was happy with.... Continue
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