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pro vyhledávání: '"Crnković, Ivica"'
Background: Data errors are a common challenge in machine learning (ML) projects and generally cause significant performance degradation in ML-enabled software systems. To ensure early detection of erroneous data and avoid training ML models using ba
Externí odkaz:
http://arxiv.org/abs/2103.04095
Heterogeneous computing is one of the most important computational solutions to meet rapidly increasing demands on system performance. It typically allows the main flow of applications to be executed on a CPU while the most computationally intensive
Externí odkaz:
http://arxiv.org/abs/2012.00594
The most important way to achieve higher performance in computer systems is through heterogeneous computing, i.e., by adopting hardware platforms containing more than one type of processor, such as CPUs, GPUs, and FPGAs. Several types of algorithms c
Externí odkaz:
http://arxiv.org/abs/2005.08712
Artificial intelligence (AI) and machine learning (ML) are increasingly broadly adopted in industry, However, based on well over a dozen case studies, we have learned that deploying industry-strength, production quality ML models in systems proves to
Externí odkaz:
http://arxiv.org/abs/2001.07522
Autor:
Andrade, Hugo, Crnkovic, Ivica
The increasing demands for computing performance have been a reality regardless of the requirements for smaller and more energy efficient devices. Throughout the years, the strategy adopted by industry was to increase the robustness of a single proce
Externí odkaz:
http://arxiv.org/abs/1905.01695
Publikováno v:
In Information and Software Technology November 2020 127
Publikováno v:
In Information and Software Technology January 2019 105:30-42
Publikováno v:
In IFAC PapersOnLine 2015 48(10):270-275
'Artech House computer library.'
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