Term

Thomas Sonderman

Overview

最終更新: 2026年7月9日

SkyWater TechnologyのCEO。IonQによる買収後も同社のブランドを維持し、引き続きCEOとして指揮を執ることが発表されている。

Mentioned Articles

1 件

Research Papers

5 件
  • Deep Transfer Learning for Traffic Sign Recognition

    Grant Rosario, Thomas Sonderman, Xingquan Zhu

    20188 件引用Semantic Scholar

    In this paper, we study using deep transfer learning to utilize knowledge gained from an already developed, large dataset of traffic signs of a specific country/region, and use this knowledge to better recognize traffic signs from another country/region, under a deep learning framework. This provides the possibility of using an already established dataset from other regions to aid in the recognition of a desired target dataset, freeing users from the burden of data gathering and labeling. We propose three deep transfer learning methods, and two of them demonstrate significantly improved accuracy compared to the simple deep learning classifier. This research shows trans ferring knowledge between deep learning classifiers can provide higher accuracy for traffic sign recognition than a model which implements only deep learning to recognize traffic signs.

  • Tissue mineral concentrations and osteochondrosis lesions in prolific sows across parities 0 through 7.

    T. Crenshaw, D. K. Schneider, C. Carlson, J. Parker, J. Sonderman, T. Ward, M. Wilson

    20138 件引用Semantic Scholar
  • CNFL: Categorical to Numerical Feature Learning for Clustering and Classification

    Eric Golinko, Thomas Sonderman, Xingquan Zhu

    20177 件引用Semantic Scholar
  • Learning Convolutional Neural Networks from Ordered Features of Generic Data

    Eric Golinko, Thomas Sonderman, Xingquan Zhu

    20187 件引用Semantic Scholar

    Convolutional neural networks (CNN) have become very popular for computer vision, text, and sequence tasks. CNNs have the advantage of being able to learn local patterns through convolution filters. However, generic datasets do not have meaningful local data correlations, because their features are assumed to be independent of each other. In this paper, we propose an approach to reorder features of a generic dataset to create feature correlations for CNN to learn feature representation, and use learned features as inputs to help improve traditional machine learning classifiers. Our experiments on benchmark data exhibit increased performance and illustrate the benefits of using CNNs for generic datasets.

  • effective implementation of APC

    M. Funk, Kevin F. Lally, R. Sundararajan, Michael L. Miller, Thomas Sonderman, J. Shriner

    20021 件引用Semantic Scholar