Advances in Neural Compression with Auke Wiggers - #570

Advances in Neural Compression with Auke Wiggers - #570

Today we’re joined by Auke Wiggers, an AI research scientist at Qualcomm. In our conversation with Auke, we discuss his team’s recent research on data compression using generative models. We discuss the relationship between historical compression research and the current trend of neural compression, and the benefit of neural codecs, which learn to compress data from examples. We also explore the performance evaluation process and the recent developments that show that these models can operate in real-time on a mobile device. Finally, we discuss another ICLR paper, “Transformer-based transform coding”, that proposes a vision transformer-based architecture for image and video coding, and some of his team’s other accepted works at the conference. The complete show notes for this episode can be found at twimlai.com/go/570

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How a Global Energy Company Adopts ML & AI with Nicholas Osborn - TWiML Talk #150

How a Global Energy Company Adopts ML & AI with Nicholas Osborn - TWiML Talk #150

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Problem Formulation for Machine Learning with Romer Rosales - TWiML Talk #149

Problem Formulation for Machine Learning with Romer Rosales - TWiML Talk #149

In this episode, i'm joined by Romer Rosales, Director of AI at LinkedIn. We begin with a discussion of graphical models and approximate probability inference, and he helps me make an important connec...

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AI for Materials Discovery with Greg Mulholland - TWiML Talk #148

AI for Materials Discovery with Greg Mulholland - TWiML Talk #148

In this episode I’m joined by Greg Mulholland, Founder and CEO of Citrine Informatics, which is applying AI to the discovery and development of new materials. Greg and I start out with an exploration ...

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Data Innovation & AI at Capital One with Adam Wenchel - TWiML Talk #147

Data Innovation & AI at Capital One with Adam Wenchel - TWiML Talk #147

In this episode I’m joined by Adam Wenchel, vice president of AI and Data Innovation at Capital One, to discuss how Machine Learning & AI are being integrated into their day-to-day practices, and how ...

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Deep Gradient Compression for Distributed Training with Song Han - TWiML Talk #146

Deep Gradient Compression for Distributed Training with Song Han - TWiML Talk #146

On today’s show I chat with Song Han, assistant professor in MIT’s EECS department, about his research on Deep Gradient Compression. In our conversation, we explore the challenge of distributed traini...

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Masked Autoregressive Flow for Density Estimation with George Papamakarios - TWiML Talk #145

Masked Autoregressive Flow for Density Estimation with George Papamakarios - TWiML Talk #145

In this episode, University of Edinburgh Phd student George Papamakarios and I discuss his paper “Masked Autoregressive Flow for Density Estimation.” George walks us through the idea of Masked Autoreg...

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Training Data for Computer Vision at Figure Eight with Qazaleh Mirsharif - TWiML Talk #144

Training Data for Computer Vision at Figure Eight with Qazaleh Mirsharif - TWiML Talk #144

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Agile Data Science with Sarah Aerni - TWiML Talk #143

Agile Data Science with Sarah Aerni - TWiML Talk #143

Today we continue our TrainAI series with Sarah Aerni, Director of Data Science at Salesforce Einstein. Sarah and I sat down at the TrainAI conference to discuss her talk “Notes from the Field: The Pl...

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