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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
General Methods For Analyzing Machine Learning Sample Complexity, Christoph Cornelius Michael
General Methods For Analyzing Machine Learning Sample Complexity, Christoph Cornelius Michael
Dissertations, Theses, and Masters Projects
During the past decade, there has been a resurgence of interest in applying mathematical methods to problems in artificial intelligence. Much work has been done in the field of machine learning, but it is not always clear how the results of this research should be applied to practical problems. Our aim is to help bridge the gap between theory and practice by addressing the question: "If we are given a machine learning algorithm, how should we go about formally analyzing it?" as opposed to the usual question: "how do we write a learning algorithm we can analyze?".;We will consider algorithms …
Causal Synchrony In The Design Of Distributed Programs, Sandra L. Peterson
Causal Synchrony In The Design Of Distributed Programs, Sandra L. Peterson
Dissertations, Theses, and Masters Projects
The outcome of any computation is determined by the order of the events in the computation and the state of the component variables of the computation at those events. The level of knowledge that can be obtained about event order and process state influences protocol design and operation. In a centralized system, the presence of a physical clock makes it easy to determine event order. It is a more difficult task in a distributed system because there is normally no global time. Hence, there is no common time reference to be used for ordering events. as a consequence, distributed protocols …
Compilation Techniques For Irregular Problems On Parallel Machines, Subhendu Das
Compilation Techniques For Irregular Problems On Parallel Machines, Subhendu Das
Dissertations, Theses, and Masters Projects
Massively parallel computers have ushered in the era of teraflop computing. Even though large and powerful machines are being built, they are used by only a fraction of the computing community. The fundamental reason for this situation is that parallel machines are difficult to program. Development of compilers that automatically parallelize programs will greatly increase the use of these machines.;A large class of scientific problems can be categorized as irregular computations. In this class of computation, the data access patterns are known only at runtime, creating significant difficulties for a parallelizing compiler to generate efficient parallel codes. Some compilers with …