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Skip to Search Results- 1Blount, Douglas
- 1Fadic Eulefi, Anton
- 1Kouritzin, Michael
- 1Li, Xiangfei
- 1McCrosky, Jesse
- 1Namiiro, Sarah Aminah
- 5Graduate and Postdoctoral Studies (GPS), Faculty of
- 5Graduate and Postdoctoral Studies (GPS), Faculty of/Theses and Dissertations
- 1Mathematical and Statistical Sciences, Department of
- 1Mathematical and Statistical Sciences, Department of/Research Publications (Mathematical and Statistical Sciences)
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2004
McCrosky, Jesse, Kouritzin, Michael, Blount, Douglas
A hybrid weighted/interacting particle filter, the selectively resampling particle (SERP) filter, is used to detect and track an unknown number of independent targets on a one-dimensional \"racetrack\" domain. The targets evolve in a nonlinear manner. The observations model a sensor positioned...
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Developing gridded climate data using neural networks: high-resolution historical climate and future projections for Africa
DownloadFall 2024
Databases of high-resolution interpolated climate data are essential for climate change research, such as analyzing impacts of climate extreme events on biological systems and the development of climate change adaptation strategies for managed and natural ecosystems. To enable such efforts, this...
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Fall 2019
Emergent communication is a framework for machine language acquisition that has recently been utilized to train deep neural networks to develop shared languages from scratch and use these languages to communicate and cooperate. Previous work on emergent communication has utilized gradient-based...
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Neural networks modelling of stream nitrogen using remote sensing information: model development and application
DownloadFall 2009
In remotely located forest watersheds, monitoring nitrogen (N) in streams often is not feasible because of the high costs and site inaccessibility. Therefore, modelling tools that can predict N in unmonitored watersheds are urgently needed to support management decisions for these watersheds....
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Fall 2018
This work contains investigations relevant for the study of catalytic chemical reactors using the detailed microkinetics approach, with the intent of improving the prediction of the product distribution. The study is comprised of two main topics. The first one consists of an investigation of...
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Fall 2023
Oftentimes, machine learning applications using neural networks involve solving discrete optimization problems, such as in pruning, parameter-isolation-based continual learning and training of binary networks. Still, these discrete problems are combinatorial in nature and are also not amenable to...