Scott Fujimoto's guest appearances

Scott Fujimoto

The DOCTORGOALS Podcast

TalkRL: The Reinforcement Learning Podcast

Kevin Mullins

Kamyar Azizzadenesheli

Jessica Hamrick

Samuel Castro

Ashley Hill

Michael Littman

Natasha Jaques

Rohin Shah

Robert Osazuwa Ness

Jordan Terry

Amy Zhang

Lauren Powell

Renee Rodriguez

Mike Herring

Dustin Harris

OwlTail

Dr. Scott Fujimoto is an interventional radiologist. He's apart of a "rare" breed in this thriving, growing specialty in radiology. After MANY years in training, he became an attending in his late 30s. He’s also the residency program director at his department in California, a BIG accomplishment! Would you be surprised if I told you that he was a DO?  What it’s like be a DO in a specialty populated by MDs What really is the difference between a DO and an MD? Which should you choose?  We work so hard to become a physician. Does it really matter which two letters of the alphabet follow our name? Rate. Review. SUBSCRIBE!   Dark room reads:  Follow Dr. Scott Fujimoto Follow DOCTORGOALS Rep' DOCTORGOALS    Shoutout to today's sponsor: ExamGuru - Use the code 'DOCTORGOALS' to get 20% OFF any and all of their *high yield* question banks for Step 2 CK and shelf exams. examguru.com

Dr. Scott Fujimoto is an interventional radiologist. He's apart of a "rare" breed in this thriving, growing specialty in radiology. After MANY years in training, he became an attending in his late 30s. He’s also the residency program director at his department in California, a BIG accomplishment! Would you be surprised if I told you that he was a DO? <ul> <li>What it’s like be a DO in a specialty populated by MDs</li> <li>What really is the difference between a DO and an MD?</li> <li>Which should you choose?</li> </ul> We work so hard to become a physician. Does it really matter which two letters of the alphabet follow our name? Rate. Review. SUBSCRIBE! Dark room reads: <ul> <li><a href="https://www.instagram.com/docfuji/" rel="nofollow" target="_blank">Follow Dr. Scott Fujimoto</a></li> <li><a href="https://www.instagram.com/doctorgoals/" rel="nofollow" target="_blank">Follow DOCTORGOALS</a></li> <li><a href="https://www.doctorgoals.com/store/" rel="nofollow" target="_blank">Rep' DOCTORGOALS</a></li> </ul> Shoutout to today's sponsor: ExamGuru - Use the code 'DOCTORGOALS' to get 20% OFF any and all of their *high yield* question banks for Step 2 CK and shelf exams. <a href="https://examguru.com/" rel="nofollow" target="_blank">examguru.com</a>

Being a DO in a World of MDs With Interventional Radiologist Dr. Scott Fujimoto

Scott Fujimoto is a PhD student at McGill University and Mila. He is the author of TD3 as well as some of the recent developments in batch deep reinforcement learning.  Featured References Addressing Function Approximation Error in Actor-Critic Methods Scott Fujimoto, Herke van Hoof, David Meger Off-Policy Deep Reinforcement Learning without Exploration Scott Fujimoto, David Meger, Doina Precup Benchmarking Batch Deep Reinforcement Learning Algorithms Scott Fujimoto, Edoardo Conti, Mohammad Ghavamzadeh, Joelle Pineau Additional References   Striving for Simplicity in Off-Policy Deep Reinforcement Learning Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi   Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine   Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, Rosalind Picard   Continuous control with deep reinforcement learning Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra   Distributed Distributional Deterministic Policy Gradients Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy Lillicrap  

Scott Fujimoto is a PhD student at McGill University and Mila. He is the author of TD3 as well as some of the recent developments in batch deep reinforcement learning.  Featured References  <a href="https://arxiv.org/abs/1802.09477" rel="nofollow" target="_blank">Addressing Function Approximation Error in Actor-Critic Methods</a> Scott Fujimoto, Herke van Hoof, David Meger <a href="https://arxiv.org/abs/1812.02900" rel="nofollow" target="_blank">Off-Policy Deep Reinforcement Learning without Exploration</a> Scott Fujimoto, David Meger, Doina Precup <a href="https://arxiv.org/abs/1910.01708" rel="nofollow" target="_blank">Benchmarking Batch Deep Reinforcement Learning Algorithms</a> Scott Fujimoto, Edoardo Conti, Mohammad Ghavamzadeh, Joelle Pineau  Additional References <ul> <li> <a href="https://arxiv.org/abs/1907.04543" rel="nofollow" target="_blank">Striving for Simplicity in Off-Policy Deep Reinforcement Learning</a> Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi </li> <li> <a href="https://arxiv.org/abs/1801.01290" rel="nofollow" target="_blank">Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor</a> Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine </li> <li> <a href="https://arxiv.org/abs/1907.00456" rel="nofollow" target="_blank">Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog  </a>Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, Rosalind Picard </li> <li> <a href="https://arxiv.org/abs/1509.02971" rel="nofollow" target="_blank">Continuous control with deep reinforcement learning</a> Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra </li> <li> <a href="https://arxiv.org/abs/1804.08617" rel="nofollow" target="_blank">Distributed Distributional Deterministic Policy Gradients</a> Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy Lillicrap </li> </ul>