Zobrazeno 1 - 10
of 131
pro vyhledávání: '"Andersen, David G."'
Autor:
Maas, Martin1 (AUTHOR) mmaas@google.com, Andersen, David G.2 (AUTHOR) dga@cs.cmu.edu, Isard, Michael3 (AUTHOR) misard@google.com, Javanmard, Mohammad Mahdi4 (AUTHOR) mjavanmard@meta.com, McKinley, Kathryn S.5 (AUTHOR) ksmckinley@google.com, Raffel, Colin6 (AUTHOR) craffel@cs.unc.edu
Publikováno v:
Communications of the ACM. Apr2024, Vol. 67 Issue 4, p87-96. 10p.
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
We present a way to rapidly bootstrap object detection on unseen videos using minimal human annotations. We accomplish this by combining two complementary sources of knowledge (one generic and the other specific) using bounding box merging and model
Externí odkaz:
http://arxiv.org/abs/1812.03626
It is commonly believed that datacenter networking software must sacrifice generality to attain high performance. The popularity of specialized distributed systems designed specifically for niche technologies such as RDMA, lossless networks, FPGAs, a
Externí odkaz:
http://arxiv.org/abs/1806.00680
The performance and efficiency of distributed machine learning (ML) depends significantly on how long it takes for nodes to exchange state changes. Overly-aggressive attempts to reduce communication often sacrifice final model accuracy and necessitat
Externí odkaz:
http://arxiv.org/abs/1802.07389
Autor:
Abadi, Martín, Andersen, David G.
We ask whether neural networks can learn to use secret keys to protect information from other neural networks. Specifically, we focus on ensuring confidentiality properties in a multiagent system, and we specify those properties in terms of an advers
Externí odkaz:
http://arxiv.org/abs/1610.06918
NetMemex explores efficient network traffic archival without any loss of information. Unlike NetFlow-like aggregation, NetMemex allows retrieving the entire packet data including full payload, which makes it useful in forensic analysis, networked and
Externí odkaz:
http://arxiv.org/abs/1603.04387