Search Results for author: Joonho Lee

Found 8 papers, 3 papers with code

A Phaseless Auxiliary-Field Quantum Monte Carlo Perspective on the Uniform Electron Gas at Finite Temperatures: Issues, Observations, and Benchmark Study

no code implementations22 Dec 2020 Joonho Lee, Miguel A. Morales, Fionn D. Malone

We investigate the viability of the phaseless finite temperature auxiliary field quantum Monte Carlo (ph-FT-AFQMC) method for ab initio systems using the uniform electron gas as a model.

Chemical Physics Strongly Correlated Electrons

Even more efficient quantum computations of chemistry through tensor hypercontraction

no code implementations6 Nov 2020 Joonho Lee, Dominic Berry, Craig Gidney, William J. Huggins, Jarrod R. McClean, Nathan Wiebe, Ryan Babbush

We describe quantum circuits with only $\widetilde{\cal O}(N)$ Toffoli complexity that block encode the spectra of quantum chemistry Hamiltonians in a basis of $N$ arbitrary (e. g., molecular) orbitals.

Quantum Physics Chemical Physics

Learning Quadrupedal Locomotion over Challenging Terrain

1 code implementation21 Oct 2020 Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, Marco Hutter

The trained controller has taken two generations of quadrupedal ANYmal robots to a variety of natural environments that are beyond the reach of prior published work in legged locomotion.

ProbAct: A Probabilistic Activation Function for Deep Neural Networks

1 code implementation26 May 2019 Kumar Shridhar, Joonho Lee, Hideaki Hayashi, Purvanshi Mehta, Brian Kenji Iwana, Seokjun Kang, Seiichi Uchida, Sheraz Ahmed, Andreas Dengel

We show that ProbAct increases the classification accuracy by +2-3% compared to ReLU or other conventional activation functions on both original datasets and when datasets are reduced to 50% and 25% of the original size.

Image Classification

Learning agile and dynamic motor skills for legged robots

1 code implementation24 Jan 2019 Jemin Hwangbo, Joonho Lee, Alexey Dosovitskiy, Dario Bellicoso, Vassilios Tsounis, Vladlen Koltun, Marco Hutter

In the present work, we introduce a method for training a neural network policy in simulation and transferring it to a state-of-the-art legged system, thereby leveraging fast, automated, and cost-effective data generation schemes.

Legged Robots

Robust Recovery Controller for a Quadrupedal Robot using Deep Reinforcement Learning

no code implementations22 Jan 2019 Joonho Lee, Jemin Hwangbo, Marco Hutter

We experimentally validate our approach on the quadrupedal robot ANYmal, which is a dog-sized quadrupedal system with 12 degrees of freedom.

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