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Publication	Type	Year
Zero-Truncated Poisson Regression for Multiway Count Data Conference on Random Matrix Theory and Numerical Linear Algebra Oscar Lopez, Daniel Dunlavy, Richard B. Lehoucq	Conference Poster – 2022 Conference Poster	2022
Low-Rank Tensor Decompositions for Large Sparse Count Data 8th European Seminar on Computing (ESCO 2022) - ONLINE Daniel Dunlavy, Richard B. Lehoucq, Oscar Lopez	Conference Presentation – 2022 Conference Presentation	2022
A Hybrid Method for Tensor Decompositions that Leverages Stochastic and Deterministic Optimization Jeremy Myers, Daniel Dunlavy https://www.osti.gov/search/identifier:1865529	Report – 2022 Report	2022
Cyclic GCP-CPAPR Hybrid SIAM Conference on Parallel Processing for Scientific Computing (PP22) Jeremy Myers, Daniel Dunlavy	Conference Presentation – 2022 Conference Presentation	2022
Zero-Truncated Poisson Tensor Decomposition for Sparse Count Data Oscar Lopez, Richard B. Lehoucq, Daniel Dunlavy https://www.osti.gov/search/identifier:1841834	Report – 2022 Report	2022
Document Retrieval and Ranking using Similarity Graph Mean Hitting Times Daniel Dunlavy, Peter Alexander Chew https://www.osti.gov/search/identifier:1835671	SAND Report – 2021 SAND Report	2021
Performance-Portable Sparse Tensor Decomposition Kernels on Emerging Parallel Architectures 2021 IEEE High Performance Extreme Computing Virtual Conference Sean Isaac Geronimo Anderson, Keita Teranishi, Daniel Dunlavy, Jee Choi	Conference Presentation – 2021 Conference Presentation	2021
Using Computation Effectively for Scalable Poisson Tensor Factorization: Comparing Methods Beyond Computational Efficiency 2021 IEEE High Performance Extreme Computing Virtual Conference Jeremy Myers, Daniel Dunlavy	Conference Presentation – 2021 Conference Presentation	2021
Performance Portability of an SpMV Kernel Across Scientific Computing and Data Science Applications 2021 IEEE High Performance Extreme Computing Virtual Conference Stephen Lecler Olivier, Nathan David Ellingwood, Jonathan W. Berry, Daniel Dunlavy	Conference Presentation – 2021 Conference Presentation	2021
Using Computation Effectively for Scalable Poisson Tensor Factorization: Comparing Methods Beyond Computational Efficiency 2021 IEEE High Performance Extreme Computing Virtual Conference Jeremy Myers, Daniel Dunlavy	Conference Paper – 2021 Conference Paper	2021
Performance Portability of an SpMV Kernel Across Scientific Computing and Data Science Applications 2021 IEEE High Performance Extreme Computing Virtual Conference Stephen Lecler Olivier, Nathan David Ellingwood, Jonathan W. Berry, Daniel Dunlavy	Conference Paper – 2021 Conference Paper	2021
Performance-Portable Sparse Tensor Decomposition Kernels on Emerging Parallel Architectures 2021 IEEE High Performance Extreme Computing Virtual Conference Sean Isaac Geronimo Anderson, Keita Teranishi, Daniel Dunlavy, Jee Choi	Conference Paper – 2021 Conference Paper	2021
Questa High Performance Data Analytics 2020 Sandia CIS ERB Reivew Daniel Dunlavy, Daniel Dunlavy https://www.osti.gov/search/identifier:1855745	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
Parameter Sensitivity Analysis of the SparTen High Performance Sparse Tensor Decomposition Software: Extended Analysis Jeremy Myers, Daniel Dunlavy, Keita Teranishi, David S Hollman https://www.osti.gov/search/identifier:1706215	Report – 2020 Report	2020
Joint Analysis of Program Data Representations using Machine Learning for Improved Software Assurance and Development Capabilities Scott Heidbrink, Kathryn Nicole Rodhouse, Daniel Dunlavy, Alexis Cooper, Xin Zhou https://www.osti.gov/search/identifier:1670527	SAND Report – 2020 SAND Report	2020
Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models Alexis Cooper, Xin Zhou, Scott Heidbrink, Daniel Dunlavy https://www.osti.gov/search/identifier:1668457	Report – 2020 Report	2020
SparTen: Leveraging Kokkos for On-node Parallelism in a Second-Order Method for Fitting Canonical Polyadic Tensor Models to Poisson Data 2020 IEEE High Performance Extreme Computing Virtual Conference Keita Teranishi, Daniel Dunlavy, Jeremy Myers, Richard Frederick Barrett https://www.osti.gov/search/identifier:1822300	Conference Paper – 2020 Conference Paper	2020
Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models Alexis Cooper, Xin Zhou, Daniel Dunlavy, Scott Heidbrink https://www.osti.gov/search/identifier:1668929	SAND Report – 2020 SAND Report	2020
Multimodal Deep Learning for Flaw Detection in Software Programs Scott Heidbrink, Kathryn Nicole Rodhouse, Daniel Dunlavy https://www.osti.gov/search/identifier:1660805	Report – 2020 Report	2020
Parameter Sensitivity Analysis of the SparTen High Performance Sparse Tensor Decomposition Software 2020 IEEE High Performance Extreme Computing Conference Jeremy Myers, Daniel Dunlavy, Keita Teranishi, David S Hollman https://www.osti.gov/search/identifier:1820264	Conference Paper – 2020 Conference Paper	2020
Multimodal Deep Learning for Flaw Detection in Software Programs Sandia MLDL Workshop Scott Heidbrink, Daniel Dunlavy, Kathryn Nicole Rodhouse https://www.osti.gov/search/identifier:1811608	Presentation (non-conference) – 2020 Presentation (non-conference)	2020
Tensor Decompositions for Analyzing Multi-Way Data CSRI Summer Program - Seminar Series Daniel Dunlavy https://www.osti.gov/search/identifier:1809204	Presentation (non-conference) – 2020 Presentation (non-conference)	2020
Topic Modeling With Natural Language Processing For Identification Of Nuclear Proliferation-relevant Scientific And Technical Publications INMM Annual Meeting Jonathan Bisila, Daniel Dunlavy, Zoe Nellie Gastelum, Craig D. Ulmer https://www.osti.gov/search/identifier:1805335	Conference Paper – 2020 Conference Paper	2020
SparTen: Leveraging Kokkos for On-node Parallelism in a Second-Order Method for Fitting Canonical Polyadic Tensor Models to Poisson Data 2020 IEEE High Performance Extreme Computing Conference (HPEC ?20) Keita Teranishi, Daniel Dunlavy, Jeremy Myers, Richard Frederick Barrett https://www.osti.gov/search/identifier:1798425	Conference Paper – 2020 Conference Paper	2020
Parameter Sensitivity Analysis of the SparTen High Performance Sparse Tensor Decomposition Software 2020 IEEE High Performance Extreme Computing Conference (HPEC'20) Jeremy Myers, Daniel Dunlavy, Keita Teranishi, David S Hollman https://www.osti.gov/search/identifier:1798424	Conference Paper – 2020 Conference Paper	2020
Document Title	Type	Year

Results 1–25 of 135

Publications

Zero-Truncated Poisson Regression for Multiway Count Data

Low-Rank Tensor Decompositions for Large Sparse Count Data

A Hybrid Method for Tensor Decompositions that Leverages Stochastic and Deterministic Optimization

Cyclic GCP-CPAPR Hybrid

Zero-Truncated Poisson Tensor Decomposition for Sparse Count Data

Document Retrieval and Ranking using Similarity Graph Mean Hitting Times

Performance-Portable Sparse Tensor Decomposition Kernels on Emerging Parallel Architectures

Using Computation Effectively for Scalable Poisson Tensor Factorization: Comparing Methods Beyond Computational Efficiency

Performance Portability of an SpMV Kernel Across Scientific Computing and Data Science Applications

Using Computation Effectively for Scalable Poisson Tensor Factorization: Comparing Methods Beyond Computational Efficiency

Performance Portability of an SpMV Kernel Across Scientific Computing and Data Science Applications

Performance-Portable Sparse Tensor Decomposition Kernels on Emerging Parallel Architectures

Questa High Performance Data Analytics

Parameter Sensitivity Analysis of the SparTen High Performance Sparse Tensor Decomposition Software: Extended Analysis

Joint Analysis of Program Data Representations using Machine Learning for Improved Software Assurance and Development Capabilities

Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models

SparTen: Leveraging Kokkos for On-node Parallelism in a Second-Order Method for Fitting Canonical Polyadic Tensor Models to Poisson Data

Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models

Multimodal Deep Learning for Flaw Detection in Software Programs

Parameter Sensitivity Analysis of the SparTen High Performance Sparse Tensor Decomposition Software

Multimodal Deep Learning for Flaw Detection in Software Programs

Tensor Decompositions for Analyzing Multi-Way Data

Topic Modeling With Natural Language Processing For Identification Of Nuclear Proliferation-relevant Scientific And Technical Publications

SparTen: Leveraging Kokkos for On-node Parallelism in a Second-Order Method for Fitting Canonical Polyadic Tensor Models to Poisson Data

Parameter Sensitivity Analysis of the SparTen High Performance Sparse Tensor Decomposition Software