The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Natural Graph Networks with Taco Cohen - #440

Dec 21, 2020

Taco Cohen is a Machine Learning Researcher at Qualcomm Technologies, known for his work on equivariant networks and video compression. In this conversation, he introduces his paper on Natural Graph Networks and the concept of 'naturality,' which proposes that relaxed constraints can lead to more versatile architectures. Taco shares insights on the integration of symmetries from physics in AI, recent advances in efficient GCNNs for mobile, and innovative techniques in neural compression that significantly enhance data efficiency.

58:23

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Episode guests

Taco Cohen

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Episode notes

Intro

2min

From Game Development to AI Exploration

5min

Symmetries in Machine Learning

23min

Implementing Equivariance in Neural Networks through Weight Sharing

2min

Advances in Efficient GCNNs for Mobile Devices

5min

Exploring Gauge Theory and Video Compression in Deep Learning

2min

Revolutionizing Data Compression with Neural Networks

15min

Challenges and Innovations in On-Device AI Applications

5min

Today we kick off our NeurIPS 2020 series joined by Taco Cohen, a Machine Learning Researcher at Qualcomm Technologies.

In our conversation with Taco, we discuss his current research in equivariant networks and video compression using generative models, as well as his paper “Natural Graph Networks,” which explores the concept of “naturality, a generalization of equivariance” which suggests that weaker constraints will allow for a “wider class of architectures.”

We also discuss some of Taco’s recent research on neural compression and a very interesting visual demo for equivariance CNNs that Taco and the Qualcomm team released during the conference.

The complete show notes for this episode can be found at twimlai.com/go/440.

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