A few QA’s from the course F’20 <Deep Learning>

i’ve just finished teaching <Deep Learning> this semester together with Yann and Alfredo. the course was in a “blended mode”, implying that lectures were given in person and live-streamed with a limited subset of students allowed to join each week and all the other students joining remotely via Zoom. this has resulted in more active online discussion among students, instructors and assistants over the course, and indeed there were quite a few interesting questions posted on the course page which was run on campuswire. i enjoyed answering those questions, because they made me think quite a bit about them myself.

Scaling laws of recovering Bernoulli

[Initial posting on Nov 29 2020][Updated on Nov 30 2020] added a section about the scaling law w.r.t. the model size, per request from Felix Hill. [Updated on Dec 1 2020] added a paragraph referring to Dauphin & Bengio’s “Big Neural Networks Waste Capacity“. this is a short post on why i thought (or more like imagined) the scaling laws from <scaling laws for autoregressive generative modeling> by Heninghan et al. “[is] inevitable from using log loss (the reducible part of KL(p||q))” when “the log loss [was used] with a max entropy model“, which was my response to Tim Dettmers’s

Creating an encyclopedia from GPT-3 using B̶a̶y̶e̶s̶’̶ ̶R̶u̶l̶e̶ Gibbs sampling

[WARNING: there is nothing “WOW” nor technical about this post, but a piece of thought i had about GPT-3 and few-shot learning.] Many aspects of OpenAI’s GPT-3 have fascinated and continue to fascinate people, including myself. these aspects include the sheer scale, both in terms of the number of parameters, the amount of compute and the size of data, the amazing infrastructure technology that has enabled training this massive model, etc. of course, among all these fascinating aspects, meta-learning, or few-shot learning, seems to be the one that fascinates people most. the idea behind this observation of GPT-3 as a

Social impacts & bias of AI

Update on October 23 2020: After I wrote this post, i was invited to give a talk on this topic of social impacts & bias of AI at the course <Ethics in AI> by Prof. Alice Oh at KAIST. I’m sharing the slide set here: Unreasonably shallow deep learning [slides]. There have been a series of news articles in Korea about AI and its applications that have been worrying me for sometime. I’ve often ranted about them on social media, but I was told that my rant alone is not enough, because it does not tell others why I ranted about

Soft k-NN

[this post was originally posted here in March 2020 and has been ported here for easier access.] TL;DR: after all, isn’t $k$-NN all we do? in my course, i use $k$-NN as a bridge between a linear softmax classifier and a deep neural net via an adaptive radial basis function network. until this year, i’ve been considering the special case of $k=1$, i.e., 1-NN, only and from there on moved to the adaptive radial basis function network. i decided however to show them how $k$-NN with $k > 1$ could be implemented as a sequence of computational layers this year,