Zobrazeno 1 - 10
of 289
pro vyhledávání: '"Ballard, Andrew"'
Autor:
Liu, Chuan-Hong, Ballard, Andrew, Olaya, David, Schmidt, Daniel R., Biesecker, John, Lucas, Tammy, Ullom, Joel, Patel, Shravan, Rafferty, Owen, Opremcak, Alexander, Dodge, Kenneth, Iaia, Vito, McBroom, Tianna, Dubois, Jonathan L., Hopkins, Pete F., Benz, Samuel P., Plourde, Britton L. T., McDermott, Robert
Publikováno v:
PRX Quantum 4, 030310 (2023)
The single flux quantum (SFQ) digital superconducting logic family has been proposed for the scalable control of next-generation superconducting qubit arrays. In the initial implementation, SFQ-based gate fidelity was limited by quasiparticle (QP) po
Externí odkaz:
http://arxiv.org/abs/2301.05696
Autor:
Liu, Chuan-Hong, Harrison, David C., Patel, Shravan, Wilen, Christopher D., Rafferty, Owen, Shearrow, Abigail, Ballard, Andrew, Iaia, Vito, Ku, Jaseung, Plourde, Britton L. T., McDermott, Robert
The ideal superconductor provides a pristine environment for the delicate states of a quantum computer: because there is an energy gap to excitations, there are no spurious modes with which the qubits can interact, causing irreversible decay of the q
Externí odkaz:
http://arxiv.org/abs/2203.06577
Autor:
Wirnsberger, Peter, Papamakarios, George, Ibarz, Borja, Racanière, Sébastien, Ballard, Andrew J., Pritzel, Alexander, Blundell, Charles
We present a machine-learning approach, based on normalizing flows, for modelling atomic solids. Our model transforms an analytically tractable base distribution into the target solid without requiring ground-truth samples for training. We report Hel
Externí odkaz:
http://arxiv.org/abs/2111.08696
Autor:
Wirnsberger, Peter, Ballard, Andrew J., Papamakarios, George, Abercrombie, Stuart, Racanière, Sébastien, Pritzel, Alexander, Rezende, Danilo Jimenez, Blundell, Charles
Free energy perturbation (FEP) was proposed by Zwanzig more than six decades ago as a method to estimate free energy differences, and has since inspired a huge body of related methods that use it as an integral building block. Being an importance sam
Externí odkaz:
http://arxiv.org/abs/2002.04913
Akademický článek
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Autor:
Battaglia, Peter W., Hamrick, Jessica B., Bapst, Victor, Sanchez-Gonzalez, Alvaro, Zambaldi, Vinicius, Malinowski, Mateusz, Tacchetti, Andrea, Raposo, David, Santoro, Adam, Faulkner, Ryan, Gulcehre, Caglar, Song, Francis, Ballard, Andrew, Gilmer, Justin, Dahl, George, Vaswani, Ashish, Allen, Kelsey, Nash, Charles, Langston, Victoria, Dyer, Chris, Heess, Nicolas, Wierstra, Daan, Kohli, Pushmeet, Botvinick, Matt, Vinyals, Oriol, Li, Yujia, Pascanu, Razvan
Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, in part, to cheap data and cheap compute resources, which have fit the
Externí odkaz:
http://arxiv.org/abs/1806.01261
Autor:
Hamrick, Jessica B., Ballard, Andrew J., Pascanu, Razvan, Vinyals, Oriol, Heess, Nicolas, Battaglia, Peter W.
Many machine learning systems are built to solve the hardest examples of a particular task, which often makes them large and expensive to run---especially with respect to the easier examples, which might require much less computation. For an agent wi
Externí odkaz:
http://arxiv.org/abs/1705.02670
Autor:
Ballard, Andrew J., Das, Ritankar, Martiniani, Stefano, Mehta, Dhagash, Sagun, Levent, Stevenson, Jacob D., Wales, David J.
Machine learning techniques are being increasingly used as flexible non-linear fitting and prediction tools in the physical sciences. Fitting functions that exhibit multiple solutions as local minima can be analysed in terms of the corresponding mach
Externí odkaz:
http://arxiv.org/abs/1703.07915
Autor:
Mirowski, Piotr, Pascanu, Razvan, Viola, Fabio, Soyer, Hubert, Ballard, Andrew J., Banino, Andrea, Denil, Misha, Goroshin, Ross, Sifre, Laurent, Kavukcuoglu, Koray, Kumaran, Dharshan, Hadsell, Raia
Learning to navigate in complex environments with dynamic elements is an important milestone in developing AI agents. In this work we formulate the navigation question as a reinforcement learning problem and show that data efficiency and task perform
Externí odkaz:
http://arxiv.org/abs/1611.03673
Autor:
Vlacich, Gregory, Ballard, Andrew, Badiyan, Shahed N., Spraker, Matthew, Henke, Lauren, Kim, Hyun, Lockhart, A. Craig, Park, Haeseong, Suresh, Rama, Huang, Yi, Robinson, Cliff G., Bradley, Jeffrey D., Samson, Pamela P.
Publikováno v:
In Clinical and Translational Radiation Oncology September 2021 30:19-25