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Frank Schoeneman

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affiliation: University at Buffalo, Department of Computer Science & Engineering, NY, USA

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Books and Theses

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2019
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Frank Schoeneman:
High Performance Approaches for Large-Scale Non-Linear Spectral Dimensionality Reduction of Scientific Data. University at Buffalo, New York, USA, 2019

Journal Articles

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2020
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Frank Schoeneman, Varun Chandola, Nils Napp, Olga Wodo, Jaroslaw Zola:
Learning Manifolds from Dynamic Process Data. Algorithms 13(2): 30 (2020)

Conference and Workshop Papers

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2019
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Frank Schoeneman, Jaroslaw Zola:
Solving All-Pairs Shortest-Paths Problem in Large Graphs Using Apache Spark. ICPP 2019: 9:1-9:10
2018
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Frank Schoeneman, Jaroslaw Zola:
Scalable Manifold Learning for Big Data with Apache Spark. IEEE BigData 2018: 272-281
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Frank Schoeneman, Varun Chandola, Nils Napp, Olga Wodo, Jaroslaw Zola:
Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes. IEEE BigData 2018: 1655-1660
2017
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Frank Schoeneman, Suchismit Mahapatra, Varun Chandola, Nils Napp, Jaroslaw Zola:
Error Metrics for Learning Reliable Manifolds from Streaming Data. SDM 2017: 750-758

Informal and Other Publications

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2019
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Frank Schoeneman, Jaroslaw Zola:
Performance of All-Pairs Shortest-Paths Solvers with Apache Spark. CoRR abs/1902.04446 (2019)
2018
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Frank Schoeneman, Varun Chandola, Nils Napp, Olga Wodo, Jaroslaw Zola:
Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes. CoRR abs/1802.06823 (2018)
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Frank Schoeneman, Jaroslaw Zola:
Scalable Manifold Learning for Big Data with Apache Spark. CoRR abs/1808.10776 (2018)

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