Zobrazeno 1 - 10
of 88
pro vyhledávání: '"Joseph, Anthony D."'
Autor:
Chasins, Sarah, Cheung, Alvin, Crooks, Natacha, Ghodsi, Ali, Goldberg, Ken, Gonzalez, Joseph E., Hellerstein, Joseph M., Jordan, Michael I., Joseph, Anthony D., Mahoney, Michael W., Parameswaran, Aditya, Patterson, David, Popa, Raluca Ada, Sen, Koushik, Shenker, Scott, Song, Dawn, Stoica, Ion
Technology ecosystems often undergo significant transformations as they mature. For example, telephony, the Internet, and PCs all started with a single provider, but in the United States each is now served by a competitive market that uses comprehens
Externí odkaz:
http://arxiv.org/abs/2205.07147
Autor:
Xin, Doris, Petersohn, Devin, Tang, Dixin, Wu, Yifan, Gonzalez, Joseph E., Hellerstein, Joseph M., Joseph, Anthony D., Parameswaran, Aditya G.
We propose opportunistic evaluation, a framework for accelerating interactions with dataframes. Interactive latency is critical for iterative, human-in-the-loop dataframe workloads for supporting exploratory data analysis. Opportunistic evaluation si
Externí odkaz:
http://arxiv.org/abs/2103.02145
Autor:
Petersohn, Devin, Macke, Stephen, Xin, Doris, Ma, William, Lee, Doris, Mo, Xiangxi, Gonzalez, Joseph E., Hellerstein, Joseph M., Joseph, Anthony D., Parameswaran, Aditya
Dataframes are a popular abstraction to represent, prepare, and analyze data. Despite the remarkable success of dataframe libraries in Rand Python, dataframes face performance issues even on moderately large datasets. Moreover, there is significant a
Externí odkaz:
http://arxiv.org/abs/2001.00888
Autor:
Hughes, J. Weston, Sittler, Taylor, Joseph, Anthony D., Olgin, Jeffrey E., Gonzalez, Joseph E., Tison, Geoffrey H.
We develop a multi-task convolutional neural network (CNN) to classify multiple diagnoses from 12-lead electrocardiograms (ECGs) using a dataset comprised of over 40,000 ECGs, with labels derived from cardiologist clinical interpretations. Since many
Externí odkaz:
http://arxiv.org/abs/1812.00497
Autor:
Stoica, Ion, Song, Dawn, Popa, Raluca Ada, Patterson, David, Mahoney, Michael W., Katz, Randy, Joseph, Anthony D., Jordan, Michael, Hellerstein, Joseph M., Gonzalez, Joseph E., Goldberg, Ken, Ghodsi, Ali, Culler, David, Abbeel, Pieter
With the increasing commoditization of computer vision, speech recognition and machine translation systems and the widespread deployment of learning-based back-end technologies such as digital advertising and intelligent infrastructures, AI (Artifici
Externí odkaz:
http://arxiv.org/abs/1712.05855
Autor:
Miller, Brad, Kantchelian, Alex, Tschantz, Michael Carl, Afroz, Sadia, Bachwani, Rekha, Faizullabhoy, Riyaz, Huang, Ling, Shankar, Vaishaal, Wu, Tony, Yiu, George, Joseph, Anthony D., Tygar, J. D.
We present and evaluate a large-scale malware detection system integrating machine learning with expert reviewers, treating reviewers as a limited labeling resource. We demonstrate that even in small numbers, reviewers can vastly improve the system's
Externí odkaz:
http://arxiv.org/abs/1510.07338
Classifier evasion consists in finding for a given instance $x$ the nearest instance $x'$ such that the classifier predictions of $x$ and $x'$ are different. We present two novel algorithms for systematically computing evasions for tree ensembles suc
Externí odkaz:
http://arxiv.org/abs/1509.07892
Autor:
Nelson, Blaine, Rubinstein, Benjamin I. P., Huang, Ling, Joseph, Anthony D., Lee, Steven J., Rao, Satish, Tygar, J. D.
Classifiers are often used to detect miscreant activities. We study how an adversary can systematically query a classifier to elicit information that allows the adversary to evade detection while incurring a near-minimal cost of modifying their inten
Externí odkaz:
http://arxiv.org/abs/1007.0484
Autor:
Nelson, Blaine, Rubinstein, Benjamin I. P., Huang, Ling, Joseph, Anthony D., Lau, Shing-hon, Lee, Steven J., Rao, Satish, Tran, Anthony, Tygar, J. D.
Classifiers are often used to detect miscreant activities. We study how an adversary can efficiently query a classifier to elicit information that allows the adversary to evade detection at near-minimal cost. We generalize results of Lowd and Meek (2
Externí odkaz:
http://arxiv.org/abs/1003.2751