Deep Image Prior

by Rehan Guha

Deep Image Prior defies the idea that “deep learning only works in the context of massive datasets or models pretrained on such datasets”. This paper showed that some deep neural networks could be successfully trained on a single image without large datasets, the structure of the network itself could be preventing deep networks from overfitting.

Zero Knowledge Proof

by Abhijit Sinha

This is a short introduction to zero knowledge proofs (ZKP), along with some motivating examples. The aim of this post is to generate curiosity among the readers for this upcoming new area of research. After giving the readers an intution of what zero knowledge proofs are, In the next posts I would then get into some technical deep dives to show how ZKP is used in the wild.

Zero Knowledge Proof of Age

by Abhijit Sinha

Age verification is an age old problem. Lots of places require you to prove that you are above a certain age to guarantee certain services, be it for issuing a drivers licence, generating a voter id etc. The current way of doing so reveals a lot of information about the user. We want to be able to do the same using Zero Knowledge Proof.

How to Measure Topic Coherence

by Haridas Narayanaswamy
Unsupervised topic modeling algorithms like LDA and NMF produces list of vocabularies for each topic after the training. These vocabs help human to assign the subject information of the topic model. So how we measure the quality of these topic words ?, this problem has to be addressed in unsupervised topic clustering algorithms like LDA / NMF to understand models are improving or not. It’s always a challenge to qualitatively measure the goodness of the words produced by each topic.


by Srikumar Subramanian

Sharing a small page I cooked up to help people explore transformations - both the linear and the non-linear kind by drawing pictures and modifying them using transformations. This hack is in the spirit of the theme of this year’s Infinity festival at Pramati - where we have “Engaging the senses” as a theme. So, engage your senses to grasp the math of transformations.