Portfolio item number 1
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Short description of portfolio item number 1
Posts by Collection
portfolio
Portfolio item number 2
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Short description of portfolio item number 2
publications
Paper Title Number 1
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1).
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Paper Title Number 2
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).
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Paper Title Number 3
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3).
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Paper Title Number 4
Published in GitHub Journal of Bugs, 2024
This paper is about fixing template issue #693.
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
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talks
AI Image Compression Demo at Imperial College London
Published:
In this video, I demonstate the first ever end-to-end AI Image compression system we build at Deep Render. This system was able to outperform BPG. This talk and demo was given at Imperial College London to a group of PhD students, Professors and CS students.
Overview of cuDNN algorithms
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In this video, I provide an overview of various cuDNN algorithms by reviewing the 2019 paper by Marc Jorda et al.
Implicit Density Estimation
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In this talk, I provide an overview of the main methods used in implicit density estimation. These methods prove to be very useful for many domains, including AI-based video compression.
Normalizing flows and VAEs
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In this video, Chris Finlay and I provide an overview of VAEs and normalizing flows through presenting the SURVAE paper which aims bridge the gap between them.
Intel Innovation Demo
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In this video, we were invited by Intel CEO Pat Gelsinger to present our AI based video compression at the Intel Innovation conference in Santa Clara.
A Conversation on AI Codecs with Jona Ballé
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AI codecs are replacing traditional block-based compression algorithms developed by standards bodies such as MPEG and ITU with fully AI-based encoder-decoder models. These AI models are trained end-to-end on video data and already outperform traditional codecs. Undoubtedly, all future codecs will be AI-powered.
Intel Innovation Award
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In this video, we presented our AI based video compression for the Intel Innovatation conference in San Jose.
Primer on AI based compression
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In this video, I provide a primer on end-to-end AI based compression. I outline the training, inference and deployment process for AI codecs. This talk is aimed at video codec experts looking to enter the AI codec space.
World’s first AI codec
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In this video, we introduce the world’s first AI codec into FFMPEG and VLC. We highlight the benifits of AI codecs and show their readiness through a demonstration on current hardware.
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.