Zobrazeno 1 - 10
of 410
pro vyhledávání: '"Andersen, P. G."'
Autor:
Andersen, Benjamin H., Safara, Francisco M. R., Grudtsyna, Valeriia, Meacock, Oliver J., Andersen, Simon G., Durham, William M., Araujo, Nuno A. M., Doostmohammadi, Amin
Collective cellular movement plays a crucial role in many processes fundamental to health, including development, reproduction, infection, wound healing, and cancer. The emergent dynamics that arise in these systems are typically thought to depend on
Externí odkaz:
http://arxiv.org/abs/2403.08466
This paper focuses on the task of detecting local episodes involving violation of the standard It\^o semimartingale assumption for financial asset prices in real time that might induce arbitrage opportunities. Our proposed detectors, defined as stopp
Externí odkaz:
http://arxiv.org/abs/2307.10872
Autor:
Andersen, P. G., Nilsson, E.
Publikováno v:
Julius-Kühn-Archiv, Iss 439, Pp 158-165 (2013)
Externí odkaz:
https://doaj.org/article/f1cf33807c9942e4803288bbfb38705d
Autor:
Zhang, Huanchen, Liu, Xiaoxuan, Andersen, David G., Kaminsky, Michael, Keeton, Kimberly, Pavlo, Andrew
We present the High-speed Order-Preserving Encoder (HOPE) for in-memory search trees. HOPE is a fast dictionary-based compressor that encodes arbitrary keys while preserving their order. HOPE's approach is to identify common key patterns at a fine gr
Externí odkaz:
http://arxiv.org/abs/2003.02391
Autor:
Jiang, Angela H., Wong, Daniel L. -K., Zhou, Giulio, Andersen, David G., Dean, Jeffrey, Ganger, Gregory R., Joshi, Gauri, Kaminksy, Michael, Kozuch, Michael, Lipton, Zachary C., Pillai, Padmanabhan
This paper introduces Selective-Backprop, a technique that accelerates the training of deep neural networks (DNNs) by prioritizing examples with high loss at each iteration. Selective-Backprop uses the output of a training example's forward pass to d
Externí odkaz:
http://arxiv.org/abs/1910.00762
Autor:
Canel, Christopher, Kim, Thomas, Zhou, Giulio, Li, Conglong, Lim, Hyeontaek, Andersen, David G., Kaminsky, Michael, Dulloor, Subramanya R.
As video camera deployments continue to grow, the need to process large volumes of real-time data strains wide area network infrastructure. When per-camera bandwidth is limited, it is infeasible for applications such as traffic monitoring and pedestr
Externí odkaz:
http://arxiv.org/abs/1905.13536
Autor:
Ratner, Alexander, Alistarh, Dan, Alonso, Gustavo, Andersen, David G., Bailis, Peter, Bird, Sarah, Carlini, Nicholas, Catanzaro, Bryan, Chayes, Jennifer, Chung, Eric, Dally, Bill, Dean, Jeff, Dhillon, Inderjit S., Dimakis, Alexandros, Dubey, Pradeep, Elkan, Charles, Fursin, Grigori, Ganger, Gregory R., Getoor, Lise, Gibbons, Phillip B., Gibson, Garth A., Gonzalez, Joseph E., Gottschlich, Justin, Han, Song, Hazelwood, Kim, Huang, Furong, Jaggi, Martin, Jamieson, Kevin, Jordan, Michael I., Joshi, Gauri, Khalaf, Rania, Knight, Jason, Konečný, Jakub, Kraska, Tim, Kumar, Arun, Kyrillidis, Anastasios, Lakshmiratan, Aparna, Li, Jing, Madden, Samuel, McMahan, H. Brendan, Meijer, Erik, Mitliagkas, Ioannis, Monga, Rajat, Murray, Derek, Olukotun, Kunle, Papailiopoulos, Dimitris, Pekhimenko, Gennady, Rekatsinas, Theodoros, Rostamizadeh, Afshin, Ré, Christopher, De Sa, Christopher, Sedghi, Hanie, Sen, Siddhartha, Smith, Virginia, Smola, Alex, Song, Dawn, Sparks, Evan, Stoica, Ion, Sze, Vivienne, Udell, Madeleine, Vanschoren, Joaquin, Venkataraman, Shivaram, Vinayak, Rashmi, Weimer, Markus, Wilson, Andrew Gordon, Xing, Eric, Zaharia, Matei, Zhang, Ce, Talwalkar, Ameet
Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains a significant obstacle, in large part due to the radically different d
Externí odkaz:
http://arxiv.org/abs/1904.03257
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