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- Machine learning (2)
- Automl (1)
- Decision tree (1)
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- Handwriting (1)
- Information Systems (1)
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Articles 1 - 6 of 6
Full-Text Articles in Physical Sciences and Mathematics
Metalearning By Exploiting Granular Machine Learning Pipeline Metadata, Brandon J. Schoenfeld
Metalearning By Exploiting Granular Machine Learning Pipeline Metadata, Brandon J. Schoenfeld
Theses and Dissertations
Automatic machine learning (AutoML) systems have been shown to perform better when they use metamodels trained offline. Existing offline metalearning approaches treat ML models as black boxes. However, modern ML models often compose multiple ML algorithms into ML pipelines. We expand previous metalearning work on estimating the performance and ranking of ML models by exploiting the metadata about which ML algorithms are used in a given pipeline. We propose a dynamically assembled neural network with the potential to model arbitrary DAG structures. We compare our proposed metamodel against reasonable baselines that exploit varying amounts of pipeline metadata, including metamodels used …
Trace: A Differentiable Approach To Line-Level Stroke Recovery For Offline Handwritten Text, Taylor Neil Archibald
Trace: A Differentiable Approach To Line-Level Stroke Recovery For Offline Handwritten Text, Taylor Neil Archibald
Theses and Dissertations
Stroke order and velocity are helpful features in the fields of signature verification, handwriting recognition, and handwriting synthesis. Recovering these features from offline handwritten text is a challenging and well-studied problem. We propose a new model called TRACE (Trajectory Recovery by an Adaptively-trained Convolutional Encoder). TRACE is a differentiable approach using a convolutional recurrent neural network (CRNN) to infer temporal stroke information from long lines of offline handwritten text with many characters. TRACE is perhaps the first system to be trained end-to-end on entire lines of text of arbitrary width and does not require the use of dynamic exemplars. Moreover, …
Ansible: Select-To-Edit For Physical Widgets, Benjamin M. Crowder
Ansible: Select-To-Edit For Physical Widgets, Benjamin M. Crowder
Theses and Dissertations
Ansible brings select-to-edit functionality to physical widgets. When programming sets of physical widgets, it can be bothersome for a programmer to remember the name of the software object that corresponds to a specific widget. Click-to-edit functionality in GUI programming provides a physical action--moving the mouse to a widget and clicking a button on the mouse--to select a virtual widget. In a similar vein, when programming physical widgets, it is natural to point at a widget and think, "I want to program that one." Ansible allows physical user interface programmers to "click" on a physical widget by making a physical action: …
Specialization: Do Your Job Well Helping Students Who Are Considering A Career In Programming Know How To Invest Their Time., Scott Pulley
Specialization: Do Your Job Well Helping Students Who Are Considering A Career In Programming Know How To Invest Their Time., Scott Pulley
Marriott Student Review
The article examines the effects of specialization on the hiring process for undergraduates studying programming whether in information systems or computer science.
Using Logical Specifications For Multi-Objective Reinforcement Learning, Kolby Nottingham
Using Logical Specifications For Multi-Objective Reinforcement Learning, Kolby Nottingham
Undergraduate Honors Theses
In the multi-objective reinforcement learning (MORL) paradigm, the relative importance of environment objectives is often unknown prior to training, so agents must learn to specialize their behavior to optimize different combinations of environment objectives that are specified post-training. These are typically linear combinations, so the agent is effectively parameterized by a weight vector that describes how to balance competing environment objectives. However, we show that behaviors can be successfully specified and learned by much more expressive non-linear logical specifications. We test our agent in several environments with various objectives and show that it can generalize to many never-before-seen specifications.
Machine Learning For Effective Parkinson's Disease Diagnosis, Brennon Brimhall
Machine Learning For Effective Parkinson's Disease Diagnosis, Brennon Brimhall
Undergraduate Honors Theses
Parkinson’s Disease is a degenerative neurological condition that affects approximately 10 million people globally. Because there is currently no cure, there is a strong motivation for research into improved and automated diagnostic procedures. Using Random Forests, a computer can effectively learn to diagnose Parkinson’s disease in a patient with high accuracy (94%), precision (95%), and recall (91%) across the data of over 2800 patients. Using similar techniques, I further determine that the most predictive medical tests relate to tremors observed in patients.