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GSoC Blog 2- Gen tutorial

4 minute read


Work in Progress Update

The last few weeks have been extremely fulfilling while I learnt more about Gen, Arviz and the entire PPL ecosystem. I’m almost close to the completion of my project and the next 2 weeks I will be focusing on winding up and making any changes required to the code. Since I have been learning to use Gen for some time now I realised it would be helpful to put a tutorial out there.

Some background about Gen

Gen is a platform for probabilistic modeling and inference. Gen includes a set of built-in languages for defining probabilistic models and a standard library for defining probabilistic inference algorithms, but is designed to be extended with an open-ended set of more specialized modeling languages and inference libraries.



Comparative study of learning approaches

Compared supervised, semi-supervised and self-supervised approaches on image classification task on STL 10 dataset. Used a ResNet-18 model for supervised, vanilla CNN for semi-supervised and SimCLR for unsupervised learning.

Meta learning library using JAX

This project aims to implement various Meta Learning methods and benchmark them on few-shot learning tasks. The goal is to build a modular extensible and well documented library of these methods in JAX.

Probing language inference models

Trained a classifier model on SNLI Dataset to determine the inference relationship between the sentence pairs and performed probing on thee model’s layers for POS tags.

Source code synthesis

Developing a model to perform code search, syntax error detection and code repair on different programming languages.




CTE Instructor-Introduction and Applications of Machine Learning

Introductory course, BITS Pilani, 2021

The course intends to help students with no experience in machine learning take their first steps in the field. We will start off with the basic Machine learning theory and look into essential tools needed for ML; libraries like numpy and pandas. Then we will proceed to teach the students about various machine learning algorithms, the maths behind it. With the help of scikit learn, the students will learn how to implement various models as well.