Ryan Poplin's guest appearances

Ryan Poplin

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

Brain Inspired

David Ferrucci

Kanaka Rajan

Alison Gopnik

Dileep George

Melanie Mitchell

Emily M. Bender

Blake Richards

Terrence Sejnowski

Sanmi Koyejo

Chelsea Finn

Dario Amodei

Jianxiong Xiao

Kate Darling

Jason Holmberg

Lotte Bransen

OwlTail

[bctt tweet=”Check out episode 6 of the Brain Inspired podcast: Deep learning, eyeballs, and brains” username=”pgmid”]   Mentioned in the show  Ryan Poplin What is a convolutional neural network? Here’s a good summary. Here’s another good summary.   Deep learning on eye images: CV risk factors can be predicted from retinal fundus images DeepVariant: Deep learning on genomics The paper: Creating a universal SNP and small indel variant caller with deep neural networks A nice, less technical summary of the work The code on Github  

[bctt tweet=”Check out episode 6 of the Brain Inspired podcast: Deep learning, eyeballs, and brains” username=”pgmid”] Mentioned in the show <ul> <li><a href="https://ai.google/research/people/105518" rel="nofollow" target="_blank">Ryan Poplin</a></li> <li>What is a convolutional neural network? <ul> <li><a href="http://cs231n.github.io/convolutional-networks/" rel="nofollow" target="_blank">Here’s</a> a good summary. <a href="https://medium.freecodecamp.org/an-intuitive-guide-to-convolutional-neural-networks-260c2de0a050" rel="nofollow" target="_blank">Here’s</a> another good summary.</li> </ul> </li> <li>Deep learning on eye images: <a href="https://www.nature.com/articles/s41551-018-0195-0" rel="nofollow" target="_blank">CV risk factors can be predicted from retinal fundus images</a></li> <li>DeepVariant: Deep learning on genomics <ul> <li>The paper: <a href="https://www.biorxiv.org/content/early/2016/12/21/092890" rel="nofollow" target="_blank">Creating a universal SNP and small indel variant caller with deep neural networks</a></li> <li>A nice, less technical <a href="https://ai.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html" rel="nofollow" target="_blank">summary of the work</a></li> <li>The <a href="http://github.com/google/deepvariant" rel="nofollow" target="_blank">code on Github</a></li> </ul> </li></ul>

BI 006 Ryan Poplin: Deep Solutions

In this episode, I'm joined by Google Research Scientist Ryan Poplin, who recently co-authored the paper “Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning.” In our conversation, Ryan details his work training a deep learning model to predict various patient risk factors for heart disease, including some surprising ones like age and gender. We also dive into some interesting findings he discovered with regards to multi-task learning, as well as his use of an attention mechanisms to provide explainability. This was a really interesting discussion that I think you’ll really enjoy! The notes for this show can be found at twimlai.com/talk/122.

In this episode, I'm joined by Google Research Scientist Ryan Poplin, who recently co-authored the paper “Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning.” In our conversation, Ryan details his work training a deep learning model to predict various patient risk factors for heart disease, including some surprising ones like age and gender. We also dive into some interesting findings he discovered with regards to multi-task learning, as well as his use of an attention mechanisms to provide explainability. This was a really interesting discussion that I think you’ll really enjoy! The notes for this show can be found at <a href="http://twimlai.com/talk/122" rel="nofollow" target="_blank">twimlai.com/talk/122</a>.

Predicting Cardiovascular Risk Factors from Eye Images with Ryan Poplin - TWiML Talk #122