Talk, Machine Learning Paper Club, University of Guelph, Virtual
I presented the DeepMind paper Hybrid computing using a neural network with dynamic external memory from 2016. This paper proposes using a multi head attention framework to train a neural network + memory model to perform computing operations. Slides available:
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:
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:
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:
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:
Tutorial, Machine Learning Research Group, University of Guelph, Ontario, Canada
I gave a tutorial on probabilistic programming using Pyro. Code is available at this Github repository https://github.com/sshkhr/ppl_tutorial. Slides below:
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:
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: