Correction to: Compiler-Assisted Instrumentation Selection for Large-Scale C++ Codes
نویسندگان
چکیده
منابع مشابه
Automatic Differentiation of C++ Codes for Large-Scale Scientific Computing
We discuss computing first derivatives for models based on elements, such as large-scale finite-element PDE discretizations, implemented in the C++ programming language. We use a hybrid technique of automatic differentiation (AD) and manual assembly, with local elementlevel derivatives computed via AD and manually summed into the global derivative. C++ templating and operator overloading work w...
متن کاملThe braneworld stability and large-scale correction in graphene like background
In this work, we consider a graphene-like background in braneworld scenario which is one of the interesting models in cosmology and theoretical physics. Indeed, this paper is an application of holography in condense matter. We use the geometric form of potential which help to obtain field equations and solve it to obtain the energy spectrum. In that case we calculate superpotential and energy d...
متن کاملQuantum Error-Correction Codes on Abelian Groups
We prove a general form of bit flip formula for the quantum Fourier transform on finite abelian groups and use it to encode some general CSS codes on these groups.
متن کاملApproximate Model Selection for Large Scale LSSVM
Model selection is critical to least squares support vector machine (LSSVM). A major problem of existing model selection approaches of LSSVM is that the inverse of the kernel matrix need to be calculated with O(n) complexity for each iteration, where n is the number of training examples. It is prohibitive for the large scale application. In this paper, we propose an approximate approach to mode...
متن کاملError - Correcting Codes and Applications to Large Scale Classification Systems
In this thesis, we study the performance of distributed output coding (DOC) and error-Correcting output coding (ECOC) as potential methods for expanding the class of tractable machine-learning problems. Using distributed output coding, we were able to scale a neural-network-based algorithm to handle nearly 10,000 output classes. In particular, we built a prototype OCR engine for Devanagari and ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Lecture Notes in Computer Science
سال: 2023
ISSN: ['1611-3349', '0302-9743']
DOI: https://doi.org/10.1007/978-3-031-23220-6_28