no code implementations • 18 Aug 2022 • Owen Lockwood, Mei Si
While there has been substantial effort and progress in understanding and working with uncertainty for supervised learning, the body of literature for uncertainty aware deep reinforcement learning is less developed.
1 code implementation • 3 Feb 2022 • Owen Lockwood
Although a variety of optimization algorithms are employed in practice, there is often a lack of theoretical or empirical motivations for this choice.
1 code implementation • 7 Sep 2021 • Owen Lockwood
Quantum Machine Learning (QML) is considered to be one of the most promising applications of near term quantum devices.
no code implementations • ICLR Workshop Rethinking_ML_Papers 2021 • Owen Lockwood
In this work, we argue that the root cause of hindrances in the accessibility of machine learning research lies not in the paper workflow but within the misaligned incentives behind the publishing and research processes.
2 code implementations • 15 Aug 2020 • Owen Lockwood, Mei Si
This work explores the potential for quantum computing to facilitate reinforcement learning problems.
4 code implementations • 6 Mar 2020 • Michael Broughton, Guillaume Verdon, Trevor McCourt, Antonio J. Martinez, Jae Hyeon Yoo, Sergei V. Isakov, Philip Massey, Ramin Halavati, Murphy Yuezhen Niu, Alexander Zlokapa, Evan Peters, Owen Lockwood, Andrea Skolik, Sofiene Jerbi, Vedran Dunjko, Martin Leib, Michael Streif, David Von Dollen, Hongxiang Chen, Shuxiang Cao, Roeland Wiersema, Hsin-Yuan Huang, Jarrod R. McClean, Ryan Babbush, Sergio Boixo, Dave Bacon, Alan K. Ho, Hartmut Neven, Masoud Mohseni
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.