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Articles 1 - 3 of 3
Full-Text Articles in Computer Engineering
Intelligent Networks For High Performance Computing, William Whitney Schonbein
Intelligent Networks For High Performance Computing, William Whitney Schonbein
Computer Science ETDs
There exists a resurgence of interest in `smart' network interfaces that can operate on data as it flows through a network. However, while smart capabilities have been expanding, what they can do for high-performance computing (HPC) is not well-understood. In this work, we advance our understanding of the capabilities and contributions of smart network interfaces to HPC. First, we show current offloaded message demultiplexing can mitigate (but not eliminate) overheads incurred by multithreaded communication. Second, we demonstrate current offloaded capabilities can be leveraged to provide Turing complete program execution on the interface. We elaborate with a framework for offloading arbitrary …
Using Uncertainty To Interpret Supervised Machine Learning Predictions, Michael C. Darling
Using Uncertainty To Interpret Supervised Machine Learning Predictions, Michael C. Darling
Electrical and Computer Engineering ETDs
Traditionally, machine learning models are assessed using methods that estimate an average performance against samples drawn from a particular distribution. Examples include the use of cross-validation or hold0out to estimate classification error, F-score, precision, and recall.
While these measures provide valuable information, they do not tell us a model's certainty relative to particular regions of the input space. Typically there are regions where the model can differentiate the classes with certainty, and regions where the model is much less certain about its predictions.
In this dissertation we explore numerous approaches for quantifying uncertainty in the individual predictions made by supervised …
Recipe For Disaster, Zac Travis
Recipe For Disaster, Zac Travis
MFA Thesis Exhibit Catalogs
Today’s rapid advances in algorithmic processes are creating and generating predictions through common applications, including speech recognition, natural language (text) generation, search engine prediction, social media personalization, and product recommendations. These algorithmic processes rapidly sort through streams of computational calculations and personal digital footprints to predict, make decisions, translate, and attempt to mimic human cognitive function as closely as possible. This is known as machine learning.
The project Recipe for Disaster was developed by exploring automation in technology, specifically through the use of machine learning and recurrent neural networks. These algorithmic models feed on large amounts of data as a …