Master’s Thesis Presentation • Systems and Networking — Optimizing MPI Collective Operations for Cloud Deployments
Zuhair AlSader, Master’s candidate
David. R. Cheriton School of Computer Science
Zuhair AlSader, Master’s candidate
David. R. Cheriton School of Computer Science
Taylor Denouden, Master’s candidate
David R. Cheriton School of Computer Science
Tiasa Mondol, Master’s candidate
David R. Cheriton School of Computer Science
Martin Gauch, Master’s candidate
David R. Cheriton School of Computer Science
Bahareh Sarrafzadeh, PhD candidate
David R. Cheriton School of Computer Science
Christian Gorenflo, PhD candidate
David R. Cheriton School of Computer Science
Sushant Agarwal, Master’s candidate
David R. Cheriton School of Computer Science
Johann Wentzel, Master’s candidate
David R. Cheriton School of Computer Science
Colin Vandenhof, Master’s candidate
David R. Cheriton School of Computer Science
Reinforcement learning (RL) is a powerful tool for developing intelligent agents, and the use of neural networks makes RL techniques more scalable to challenging real-world applications, from task-oriented dialogue systems to autonomous driving. However, one of the major bottlenecks to the adoption of RL is efficiency, as it often takes many time steps to learn an acceptable policy.
William Sigouin, Master’s candidate
David R. Cheriton School of Computer Science