Collection of talks given at the ML reading group@IIITD (Inactive)
We want to create a free flow of ideas from other fields to ML and vice-versa. Given that ML is a highly interdisciplinary field, it will really help us all to learn about good ideas from other fields or to apply techniques to other fields. We are always looking to expand and include. Although our primary focus is people active in research, we welcome all willing to develop a research expertise towards some topics and willing to present about good ideas/papers occasionally. To know more, follow this issue.
Some guidelines which we think is helpful, feel free to suggest more, or modify:
- We meet every week for around one hour.
- The focus is on good recent papers introducing novel techniques with good experimental evidence or motivating theory. You can also present an overview of a topic like Deep Gaussian Processes, High dimensional Probability, Challenges in NLP evaluation metrics, etc.
- Feel free to use material from other talks, summarize other long lectures (2-3 hour conference tutorials) as part of your presentation. Just try to get permission to use that material for your presentation from the original authors.
- As we have some people who are from different fields, please feel free to present good ideas from your areas, like Quantum Computing and ML, Signal Processing, etc.
- Please let everyone know the topic/paper link you are going to present at least 2-3 days before your slot so that we look at the paper/background material for better understanding.
- The talks will be recorded for reference.
| Date | Topic | Presenter |
|---|---|---|
| 22nd November 2020 | Bayesian Deep Learning | Mohit |
| 1st November 2020 | Geometric Deep Learning | Nikita |
| 4th October 2020 | Active Learning | Charul |
| 20th September 2020 | Normalizing Flows | Nilay |
| 5th September 2020 | DL Pipelines for Vision | Aradhya |
| 21st August 2020 | Quantum ML | Tharrmashastha S A P V |
| 7th August 2020 | Gaussian Processes | Venktesh |