Vishnu Dutt Sharma

Vishnu Dutt Sharma

Robotics and Visual Computing Researcher at Nokia Bell Labs

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Explaining Deep Reinforcement Learning models with Linear Model U-Trees

Published:

Read this post at Medium.
A popular deep learning explainability approach is to approaximate the behavior of the pre-trained deep learning model into a less complex, but interpretable learning method. Decision trees are quite useful here because they are often easy to interpret, while also providing a good performance. In this post, I summarise a explanability method using Linear Model U-Trees (LMUTs) by Guiliang Liu, Oliver Schulte, Wang Zhu and Qingcan Li in their paper Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees.

On Local Interpretable Model-agnostic Explanations

Published:

Read this post at Medium.
Local Interpretable Model-agnostic Explanations (LIME) is one of the most popular technique for deel learning explanability, as it covers a wide range of inputs types (e.g. images, text, tabular data) and treats the model as a black-box, which means it can be used with any deep learning model. In this post, I explain how LIME works with the help of some intermediate outputs.

Neural Networks: Mathematics and Interpretation

Published:

Read this post at Medium.
Neural Networks (NNs) are the basic units of deep learning model. Therefore, the first step towards understanding how deep learning models work is to understand how NNs work. In this post, I use a methoematical approach using plots and matrix algebra to shed light on how the elements of NN come together to generate a powerful learning mechanism.

Understanding what RNN learns: Part 1

Published:

Read this post at Medium.
Recurrent Neural Networks (RNNs) are difficult to explain or interpret because of both the underlying neural networks and their recurrent nature. In this post, I look at the elements of RNNs to explain how they work individually and in conjunction with other elements.

Keras Functional models: Few pointers for debugging

Published:

Read this post at Medium.
I leaned the ropes of deep learning model building with Keras. It’s easy to learn and use, and gives the user options to try many useful APIs like early stopping, scheduling learning rate, etc. As a beginner, you may want to familiarize yourself with some basics for debugging. In this post, I list down a few things that I found useful for that.

portfolio

projects

publications

Building a Word Segmenter for Sanskrit Overnight

Vikas Reddy, Amrith Krishna, Vishnu Dutt Sharma, Prateek Gupta, Vineeth M R, Pawan Goyal. (2018). "Building a Word Segmenter for Sanskrit Overnight."
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Graph Neural Networks for Decentralized Multi-Robot Target Tracking

Lifeng Zhou*, Vishnu Dutt Sharma*, Qingbiao Li, Amanda Prorok, Alejandro Ribeiro, Pratap Tokekar, Vijay Kumar (2022). "Graph Neural Networks for Decentralized Multi-Robot Target Tracking."
(*: Indicates equal contribution)
IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR 2022)

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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.