Institution
Hewlett-Packard
Company•Palo Alto, California, United States•
About: Hewlett-Packard is a company organization based out in Palo Alto, California, United States. It is known for research contribution in the topics: Signal & Substrate (printing). The organization has 34663 authors who have published 59808 publications receiving 1467218 citations. The organization is also known as: Hewlett Packard & Hewlett-Packard Company.
Papers published on a yearly basis
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18 Jun 2016TL;DR: This work proposes a novel PIM architecture, called PRIME, to accelerate NN applications in ReRAM based main memory, and distinguishes itself from prior work on NN acceleration, with significant performance improvement and energy saving.
Abstract: Processing-in-memory (PIM) is a promising solution to address the "memory wall" challenges for future computer systems. Prior proposed PIM architectures put additional computation logic in or near memory. The emerging metal-oxide resistive random access memory (ReRAM) has showed its potential to be used for main memory. Moreover, with its crossbar array structure, ReRAM can perform matrix-vector multiplication efficiently, and has been widely studied to accelerate neural network (NN) applications. In this work, we propose a novel PIM architecture, called PRIME, to accelerate NN applications in ReRAM based main memory. In PRIME, a portion of ReRAM crossbar arrays can be configured as accelerators for NN applications or as normal memory for a larger memory space. We provide microarchitecture and circuit designs to enable the morphable functions with an insignificant area overhead. We also design a software/hardware interface for software developers to implement various NNs on PRIME. Benefiting from both the PIM architecture and the efficiency of using ReRAM for NN computation, PRIME distinguishes itself from prior work on NN acceleration, with significant performance improvement and energy saving. Our experimental results show that, compared with a state-of-the-art neural processing unit design, PRIME improves the performance by ~2360× and the energy consumption by ~895×, across the evaluated machine learning benchmarks.
1,197 citations
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TL;DR: In this paper, a study of social interactions within Twitter reveals that the driver of usage is a sparse and hidden network of connections underlying the declared set of friends and followers, revealing that the linked structures of social networks do not reveal actual interactions among people.
Abstract: Scholars, advertisers and political activists see massive online social networks as a representation of social interactions that can be used to study the propagation of ideas, social bond dynamics and viral marketing, among others. But the linked structures of social networks do not reveal actual interactions among people. Scarcity of attention and the daily rythms of life and work makes people default to interacting with those few that matter and that reciprocate their attention. A study of social interactions within Twitter reveals that the driver of usage is a sparse and hidden network of connections underlying the declared set of friends and followers.
1,151 citations
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Eindhoven University of Technology1, Queensland University of Technology2, Capgemini3, University of Rome Tor Vergata4, Humboldt University of Berlin5, Software AG6, University of Padua7, Polytechnic University of Catalonia8, Hewlett-Packard9, Ghent University10, New Mexico State University11, IBM12, University of Milan13, University of Tartu14, University of Vienna15, Technical University of Lisbon16, Telecom SudParis17, Rabobank18, Infosys19, University of Calabria20, Fujitsu21, Pennsylvania State University22, University of Bari23, University of Bologna24, Vienna University of Economics and Business25, Free University of Bozen-Bolzano26, Stevens Institute of Technology27, Indian Council of Agricultural Research28, Pontifical Catholic University of Chile29, University of Haifa30, Ulsan National Institute of Science and Technology31, Cranfield University32, Katholieke Universiteit Leuven33, Deloitte34, Tsinghua University35, University of Innsbruck36, Hasso Plattner Institute37
TL;DR: This manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users to increase the maturity of process mining as a new tool to improve the design, control, and support of operational business processes.
Abstract: Process mining techniques are able to extract knowledge from event logs commonly available in today’s information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. There are two main drivers for the growing interest in process mining. On the one hand, more and more events are being recorded, thus, providing detailed information about the history of processes. On the other hand, there is a need to improve and support business processes in competitive and rapidly changing environments. This manifesto is created by the IEEE Task Force on Process Mining and aims to promote the topic of process mining. Moreover, by defining a set of guiding principles and listing important challenges, this manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users. The goal is to increase the maturity of process mining as a new tool to improve the (re)design, control, and support of operational business processes.
1,135 citations
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TL;DR: It is shown that it is possible to build a hybrid classifier that will perform at least as well as the best available classifier for any target conditions, and in some cases, the performance of the hybrid actually can surpass that of the best known classifier.
Abstract: In real-world environments it usually is difficult to specify target operating conditions precisely, for example, target misclassification costs. This uncertainty makes building robust classification systems problematic. We show that it is possible to build a hybrid classifier that will perform at least as well as the best available classifier for any target conditions. In some cases, the performance of the hybrid actually can surpass that of the best known classifier. This robust performance extends across a wide variety of comparison frameworks, including the optimization of metrics such as accuracy, expected cost, lift, precision, recall, and workforce utilization. The hybrid also is efficient to build, to store, and to update. The hybrid is based on a method for the comparison of classifier performance that is robust to imprecise class distributions and misclassification costs. The ROC convex hull (ROCCH) method combines techniques from ROC analysis, decision analysis and computational geometry, and adapts them to the particulars of analyzing learned classifiers. The method is efficient and incremental, minimizes the management of classifier performance data, and allows for clear visual comparisons and sensitivity analyses. Finally, we point to empirical evidence that a robust hybrid classifier indeed is needed for many real-world problems.
1,134 citations
Authors
Showing all 34676 results
Name | H-index | Papers | Citations |
---|---|---|---|
Andrew White | 149 | 1494 | 113874 |
Stephen R. Forrest | 148 | 1041 | 111816 |
Rafi Ahmed | 146 | 633 | 93190 |
Leonidas J. Guibas | 124 | 691 | 79200 |
Chenming Hu | 119 | 1296 | 57264 |
Robert E. Tarjan | 114 | 400 | 67305 |
Hong-Jiang Zhang | 112 | 461 | 49068 |
Ching-Ping Wong | 106 | 1128 | 42835 |
Guillermo Sapiro | 104 | 667 | 70128 |
James R. Heath | 103 | 425 | 58548 |
Arun Majumdar | 102 | 459 | 52464 |
Luca Benini | 101 | 1453 | 47862 |
R. Stanley Williams | 100 | 605 | 46448 |
David M. Blei | 98 | 378 | 111547 |
Wei-Ying Ma | 97 | 464 | 40914 |