How ‘Deep’ runs The rabbit hole

…And with what probability?

Tomas Malisiewicz talks about the balance of inference and uncertainty in a new article.



Kernels Part 1: What is an RBF Kernel? Really?

An intriguing article. To look at an RBF kernel as a low pass filter is something novel. It also basically shows why RBF kernels work brilliantly on high dimensional images. Given that your image features generally lie in a continuous domain, an RBF kernel generally can fit smooth solutions and thereby create more relevant separating hyperplanes,especially in case of multiple classes.

My first blog on machine learning is to discuss a pet peeve I have about working in the industry, namely why not to apply an RBF kernel to text classification tasks.

I wrote this as a follow up to a Quora Answer on the subject:


I will eventually re-write this entry once I get better at Latex.  For now, refer to 

Smola, Scholkopf, and Muller, The connection between regularization operators and support vector kernels  http://cbio.ensmp.fr/~jvert/svn/bibli/local/Smola1998connection.pdf

I expand on one point–why not to use Radial Basis Function (RBF) Kernels for Text Classification.  I encountered this  while a consultant a few years ago eBay, where not one but 3 of the teams (local, German, and Indian) were all doing this, with no success  They are were treating a multi-class text classification problem using an SVM with an RBF Kernel.  What is worse, they were claiming the RBF calculations…

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An Interesting History of Computer Vision

Dr. Fei Fei Li from Stanford discusses the advent and growth of computer vision in recent years. Particularly intersting is her recent research on multimodal interactions and large scale visual recognition. This has been primarily made possible due to the growth in GPU technology. I hope to try out Theano and Caffe for deep learning in this scenario soon.


Recent Publications from L. Fei Fei’s group:


Back to Basics: Sparse Coding?

A good introduction to Sparse Coding. Hope to do some stuff regarding this in the future.

the Serious Computer Vision Blog

by Gooly (Li Yang Ku)

Gabor like filters

It’s always good to go back to the reason that lured you into computer vision once in a while. Mine was to understand the brain after I astonishingly realized that computers have no intelligence while I was studying EE in undergrad. In fact if they use the translation “computer” instead of  “electrical brain” in my mother language, I would probably be better off.

Anyway, I am currently revisiting some of the first few computer vision papers I read, and to tell the truth I still learn a lot from reading stuffs I read several times before, which you can also interpret it as I never actually understood a paper.

So back to the papers,

Simoncelli, Eero P., and Bruno A. Olshausen. “Natural image statistics and neural representation.” Annual review of neuroscience 24.1 (2001): 1193-1216.

Olshausen, Bruno A., and David J. Field. “Sparse coding with an…

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The Start of An Age

It has been several years since blogging took the world by storm. And it has progressed from being a way to just put down your thoughts for the world to read to being an entirely new way to view the world.

I think it is time I started to write down my thoughts too. Let me start by introducing myself. I am Ameya Joshi, second year undergraduate at BITS Pilani,  Goa Campus, currently pursuing B.E.(Hons.) Electronics and Electrical Engineering.

Currently, I am engaged in a tussle with one of the most popular fields of computing in recent times- Artificial Intelligence. I actually got introduced to this field through Image Processing, another emerging area of computing. I am currently pursuing my interests in these fields and this blog will be a record of my work in them.