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title={Depth-Limited Solving for Imperfect-Information Games},
author={Brown, Noam and Sandholm, Tuomas and Amos, Brandon},
journal={arXiv preprint arXiv:1805.08195},
A fundamental challenge in imperfect-information games is that states do not have well-defined values. As a result, depth-limited search algorithms used in single-agent settings and perfect-information games do not apply. This paper introduces a principled way to conduct depth-limited solving in imperfect-information games by allowing the opponent to choose among a number of strategies for the remainder of the game at the depth limit. Each one of these strategies results in a different set of values for leaf nodes. This forces an agent to be robust to the different strategies an opponent may employ. We demonstrate the effectiveness of this approach by building a master-level heads-up no-limit Texas hold'em poker AI that defeats two prior top agents using only a 4-core CPU and 16 GB of memory. Developing such a powerful agent would have previously required a supercomputer.
title={OpenFace: A general-purpose face recognition
library with mobile applications},
author={Amos, Brandon and Bartosz Ludwiczuk and Satyanarayanan, Mahadev},
institution={Technical Report CMU-CS-16-118, CMU School of Computer Science},
Cameras are becoming ubiquitous in the Internet of Things (IoT) and
can use face recognition technology to improve context. There is a
large accuracy gap between today's publicly available face recognition
systems and the state-of-the-art private face recognition
systems. This paper presents our OpenFace face recognition library
that bridges this accuracy gap. We show that OpenFace provides
near-human accuracy on the LFW benchmark and present a new
classification benchmark for mobile scenarios. This paper is intended
for non-experts interested in using OpenFace and provides a light
introduction to the deep neural network techniques we use.
We released OpenFace in October 2015 as an open source library under
the Apache 2.0 license. It is available at:
title={Are Cloudlets Necessary?},
Gao, Ying and Hu, Wenlu and Ha, Kiryong and
Amos, Brandon and Pillai, Padmanabhan and
Satyanarayanan, Mahadev
institution={Technical Report CMU-CS-15-139, CMU School of Computer Science},
We present experimental results from Wi-Fi and 4G LTE networks to validate the
intuition that low end-to-end latency of cloud services improves application
response time and reduces energy consumption on mobile devices. We focus
specifically on computational offloading as a cloud service. Using a wide
range of applications, and exploring both pre-partitioned and dynamically
partitioned approaches, we demonstrate the importance of low latency for
cloud offload services. We show the best performance is achieved by
offloading to cloudlets, which are small-scale edge-located data centers. Our
results show that cloudlets can improve response times 51\% and reduce energy
consumption in a mobile device by up to 42\% compared to cloud offload.
title={Adaptive VM handoff across cloudlets},
Ha, Kiryong and Abe, Yoshihisa and Chen, Zhuo and
Hu, Wenlu and Amos, Brandon and Pillai, Padmanabhan and
Satyanarayanan, Mahadev
institution={Technical Report CMU-CS-15-113, CMU School of Computer Science},
Cloudlet offload is a valuable technique for ensuring low end-to-end latency of
resource-intensive cloud processing for many emerging mobile applications.
This paper examines the impact of user mobility on cloudlet offload, and
shows that even modest user mobility can result in significant network
degradation. We propose VM handoff as a technique for seamlessly transferring
VMencapsulated execution to a more optimal offload site as users move. Our
approach can perform handoff in roughly a minute even over limited WANs by
adaptively reducing data transferred. We present experimental results to
validate our implementation and to demonstrate effectiveness of adaptation to
changing network conditions and processing capacity
title={{{QNSTOP-QuasiNewton Algorithm for Stochastic Optimization}}},
author={Brandon Amos and David Easterling and Layne Watson and
William Thacker and Brent Castle and Michael Trosset},
QNSTOP consists of serial and parallel (OpenMP) Fortran 2003 codes for the
quasi-Newton stochastic optimization method of Castle and Trosset. For
stochastic problems, convergence theory exists for the particular
algorithmic choices and parameter values used in QNSTOP. Both the parallel
driver subroutine, which offers several parallel decomposition strategies,
and the serial driver subroutine can be used for stochastic optimization or
deterministic global optimization, based on an input switch. QNSTOP is
particularly effective for “noisy” deterministic problems, using only
objective function values. Some performance data for computational systems
biology problems is given.