On the Efficient Marginalization of Probabilistic Sequence Models

Real-world data often exhibits sequential dependence, across diverse domains such as human behavior, medicine, finance, and climate modeling. Probabilistic methods capture the inherent uncertainty associated with prediction in these contexts, with autoregressive models being especially prominent. This dissertation focuses on using autoregressive models to answer complex probabilistic queries that go beyond single-step prediction, such as the timing of future events or the likelihood of a specific event occurring before another. In particular, we develop a broad class of novel and efficient approximation techniques for marginalization in sequential models that are model-agnostic. These techniques rely solely on access to and sampling from next-step conditional distributions of a pre-trained autoregressive model, including both traditional parametric models as well as more recent neural autoregressive models. Specific approaches are presented for discrete sequential models, for marked temporal point processes, and for stochastic jump processes, each tailored to a well-defined class of informative, long-range probabilistic queries

salmon: A Symbolic Linear Regression Package for Python

One of the most attractive features of R is its linear modeling capabilities. We describe a Python package, salmon, that brings the best of R's linear modeling functionality to Python in a Pythonic way---by providing composable objects for specifying and fitting linear models. This object-oriented design also enables other features that enhance ease-of-use, such as automatic visualizations and intelligent model building

salmon: A Symbolic Linear Regression Package for Python

One of the most attractive features of R is its linear modeling capabilities. We describe a Python package, salmon, that brings the best of R's linear modeling functionality to Python in a Pythonic way - by providing composable objects for specifying and fitting linear models. This object-oriented design also enables other features that enhance easeof-use, such as automatic visualizations and intelligent model building

Tensile Test Design to Measure Interlayer Adhesion in Investment Casting Shells for Spalling Mitigation

We designed a tensile test fixture for a 112 lbf capacity Instron load frame that imparts a normal force on the face of a button epoxied to an investment casting shell sample, delaminating the shell area attached to the button. Using a green standard shell (Group 1), a partially fired standard shell (Group 2), and a green shell with a different third coat (Group 3), we verified that the fixture can measure differences in strength between sample groups. We attached steel buttons to leveled samples with 0.05 mL of Hysol-Loctite 9340 epoxy, let it cure for 48 hours, and tested them at 0.05 in./min. Most shells failed below the face coat, instead of spalling. Groups 1 and 2 failed in a backup layer, or at the larger stucco beneath it (0.035-0.044″ deep). Group 3 failed in the face coat (0.010″), flat in a backup layer (0.033″), or in rounded craters through several layers (0.064″). We measured fracture areas in Photoshop to calculate failure stresses, which averaged 116.21 psi for Group 1, 179.42 psi for Group 2, and 141.99 psi for Group 3, with respective standard deviations of 21.78 psi, 30.84 psi, and 31.21 psi. Two-sample t-tests showed statistically valid distinctions between each group’s results, indicating that this fixture could be used to further investigate designing a stronger shell to mitigate face coat spalling

Willie Lee Freeman

LCpl. Willie Lee Freeman, January 3, 1948 - May 27, 1968 Native Sons Exhibit Pagehttps://kb.gcsu.edu/nativesons/1011/thumbnail.jp

Incorporating Engineering in High School FACS and Chemistry Class

This paper presents the preliminary development of engineering units for a year-long high school Food Science and Chemistry course. While the course is not intended as an engineering course, we explored ways that students could be introduced to the engineering design process, and other engineering concepts, as a way to motivate interdisciplinary thinking and inspire future exploration. As this project is in its early stages, we will present three potential units that incorporate engineering while meeting Next Generation Science Standards and Minnesota State Science Standards related to this course

Predictive Querying for Autoregressive Neural Sequence Models

In reasoning about sequential events it is natural to pose probabilistic queries such as "when will event A occur next" or "what is the probability of A occurring before B", with applications in areas such as user modeling, medicine, and finance. However, with machine learning shifting towards neural autoregressive models such as RNNs and transformers, probabilistic querying has been largely restricted to simple cases such as next-event prediction. This is in part due to the fact that future querying involves marginalization over large path spaces, which is not straightforward to do efficiently in such models. In this paper we introduce a general typology for predictive queries in neural autoregressive sequence models and show that such queries can be systematically represented by sets of elementary building blocks. We leverage this typology to develop new query estimation methods based on beam search, importance sampling, and hybrids. Across four large-scale sequence datasets from different application domains, as well as for the GPT-2 language model, we demonstrate the ability to make query answering tractable for arbitrary queries in exponentially-large predictive path-spaces, and find clear differences in cost-accuracy tradeoffs between search and sampling methods.Comment: Oral Presentation at the Intl. Conference on Neural Information Processing Systems (NeurIPS 2022