Talks and presentations

What can neural networks reason about?

January 12, 2021

Talk, Deep Learning Paper Club, UWaterloo & Machine Learning Research Group, UoGuelph, Virtual

I gave two talks summarizing the ICLR 2020 paper What Can Neural Networks Reason About?. This paper proposes a theoretical framework to understand the role of inductive biases on the ability of neural networks to reason about problems which have algorithmic solutions. Slides available:

Abstract Visual Reasoning

October 01, 2020

Talk, Machine Learning Research Group, University of Guelph, Virtual

I gave a talk on the problem of Abstract Visual Reasoning in computer vision. We discussed ideas on generalisation across visual concepts, compositionality, the role of inductive biases, symbolic knowledge etc. Slides available:

Opacity in artificial intelligence

September 26, 2020

Presentation, University of Guelph, UNIV-6090, Virtual

For my graduate course on Artificial Intelligence and Society, I gave a presentation on the issue of opacity and the need for interpretability in artificial intelligence as well as some research directions in interpretability research. Slides available:

Artificial Cognition

September 10, 2020

Talk, Data Science Club, Indian Institute of Technology (ISM) Dhanbad, Virtual

I gave a talk on Explainable Artificial Intelligence (XAI) using methods from Cognitive Science to the Data Science Club at my alma mater, Indian Institute of Technology (ISM) Dhanbad. This talk was based on work done by advisor/collaborator Eric J Taylor and myself for our CVPR workshop paper: Response Time Analysis for Explainability of Visual Processing in CNNs. Slides available:

Dynamic Inference Models

March 01, 2020

Talk, Machine Learning Research Group, University of Guelph, Ontario, Canada

I gave a talk on dynamic inference models in deep learning i.e. models that can perform a variable amount of computation depending on the input complexity. Slides available:

Knowledge-Enabled Visual Question Answering that utilizes scene text

October 10, 2019

Talk, Machine Learning Research Group, University of Guelph, Ontario, Canada

I gave a talk on my research work on visual question answering where models can successfully utilize scene text information as well as external world knowledge from knowledge graphs. The talk was a summary of our research published at ICDAR and ICCV. Slides available: