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Randy C. Paffenroth
Randy Clinton Paffenroth
Person information
- affiliation: Worcester Polytechnic Institute, MA, USA
- affiliation (former): Numerica Corporation, Loveland, CO, USA
- affiliation (former): California Institute of Technology, Pasadena, CA, USA
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2020 – today
- 2024
- [c44]Nachiket Bapat, Randy C. Paffenroth, Raghvendra V. Cowlagi:
An Example of Synthetic Data Generation for Control Systems Using Generative Adversarial Networks: Zermelo Minimum-Time Navigation. ACC 2024: 68-73 - [i17]Quincy Hershey
, Randy C. Paffenroth, Harsh Nilesh Pathak, Simon Tavener:
Rethinking the Relationship between Recurrent and Non-Recurrent Neural Networks: A Study in Sparsity. CoRR abs/2404.00880 (2024) - 2023
- [j20]David H. Kenney, Randy C. Paffenroth, Michael T. Timko
Dimensionally reduced machine learning model for predicting single component octanol-water partition coefficients. J. Cheminformatics 15(1): 9 (2023) - [j19]Brian Lewandowski, Randy C. Paffenroth:
Autoencoder Feature Residuals for Network Intrusion Detection: One-Class Pretraining for Improved Performance. Mach. Learn. Knowl. Extr. 5(3): 868-890 (2023) - [c43]Maria Barger, Evan C. Witz, Randy C. Paffenroth:
Enhancing Neural Network Performance for Problems in the Physical Sciences: Applications to Electromagnetic Signal Source Localization. ACSSC 2023: 860-865 - [c42]Zheyi Qin, Randy C. Paffenroth, Anura P. Jayasumana:
Graph Coordinates and Conventional Neural Networks - An Alternative for Graph Neural Networks. IEEE Big Data 2023: 4456-4465 - [c41]Brian Lewandowski, Randy C. Paffenroth:
One-class Classification Using Autoencoder Feature Residuals for Improved IoT Network Intrusion Detection. ICCCN 2023: 1-7 - [c40]Alexander M. Moore, Randy C. Paffenroth, Ken T. Ngo, Joshua R. Uzarski:
ChemVise: Maximizing Out-of-Distribution Chemical Detection with a Novel Application of Transfer Learning. ICMLA 2023: 53-60 - [c39]Quincy Hershey
Exploring Neural Network Structure through Sparse Recurrent Neural Networks: A Recasting and Distillation of Neural Network Hyperparameters. ICMLA 2023: 128-135 - [c38]Harsh Nilesh Pathak
Sequentia12D: Organizing Center of Skip Connections for Transformers. ICMLA 2023: 362-368 - [c37]Maria Barger, Randy C. Paffenroth, Harsh Nilesh Pathak
Conditioned Cycles in Sparse Data Domains: Applications to Electromagnetics. ICMLA 2023: 1324-1331 - [c36]Maria Barger, Patrick Bidigare, D. Richard Brown, Ilana Heintz, Randy C. Paffenroth, Evan C. Witz:
Overwater Emitter Localization with a Single Receiver using Neural Network Modeling. ICMLA 2023: 1589-1594 - [i16]Alexander M. Moore, Randy C. Paffenroth, Ken T. Ngo, Joshua R. Uzarski:
ChemVise: Maximizing Out-of-Distribution Chemical Detection with the Novel Application of Zero-Shot Learning. CoRR abs/2302.04917 (2023) - [i15]Zheyi Qin, Randy C. Paffenroth, Anura P. Jayasumana:
Graph Coordinates and Conventional Neural Networks - An Alternative for Graph Neural Networks. CoRR abs/2312.01342 (2023) - [i14]Alexander M. Moore, Randy C. Paffenroth, Kenneth T. Ngo, Joshua R. Uzarski:
ChemTime: Rapid and Early Classification for Multivariate Time Series Classification of Chemical Sensors. CoRR abs/2312.09871 (2023) - 2022
- [j18]Kelum Gajamannage
Reconstruction of fragmented trajectories of collective motion using Hadamard deep autoencoders. Pattern Recognit. 131: 108891 (2022) - [c35]Yingnan Liu, Randy Clinton Paffenroth:
Ensemble Image Super-Resolution CNNs for Small Data and Diverse Compressive Models. ADMA (2) 2022: 89-102 - [c34]Alexander M. Moore
Cycles Improve Conditional Generators: Synthesis and Augmentation for Data Mining. ADMA (2) 2022: 352-364 - [c33]Brian Lewandowski, Randy C. Paffenroth, Kayleigh Campbell:
Improving Network Intrusion Detection Using Autoencoder Feature Residuals. ICDIS 2022: 31-39 - [c32]Alexander M. Moore, Randy C. Paffenroth, Ken T. Ngo, Joshua R. Uzarski:
ACGANs Improve Chemical Sensors for Challenging Distributions. ICMLA 2022: 286-293 - [c31]Brian Lewandowski, Randy C. Paffenroth:
Autoencoder Feature Residuals for Network Intrusion Detection: Unsupervised Pre-training for Improved Performance. ICMLA 2022: 1334-1341 - 2021
- [j17]Kelum Gajamannage
Bounded manifold completion. Pattern Recognit. 111: 107661 (2021) - [c30]Gunjan Mahindre, Rasika Karkare, Randy C. Paffenroth, Anura P. Jayasumana:
A Pre-training Oracle for Predicting Distances in Social Networks. IEEE BigData 2021: 4126-4135 - [c29]Nitish Bahadur, Randy C. Paffenroth:
Dimension Estimation using Second Order data in Finance. IEEE BigData 2021: 4551-4560 - [c28]F. Patricia Medina, Randy C. Paffenroth:
Classification frameworks comparison on 3D point clouds. HPEC 2021: 1-6 - [c27]Wenjing Li, Randy C. Paffenroth, Michael T. Timko
Theory for Deep Learning Regression Ensembles with Application to Raman Spectroscopy Analysis. ICMLA 2021: 1049-1056 - [c26]Evan C. Witz, Maria Barger
Deep Learning for Range Localization via Over-Water Electromagnetic Signals. ICMLA 2021: 1537-1544 - [c25]Yingnan Liu, Randy Clinton Paffenroth:
Voxel-based Deep Learning for Image Super-resolution of Areal Density Maps of Carbon-nanotube Sheets. ICMLA 2021: 1660-1667 - [i13]F. Patricia Medina, Randy C. Paffenroth:
Machine Learning in LiDAR 3D point clouds. CoRR abs/2101.09318 (2021) - [i12]Rasika Karkare, Randy C. Paffenroth, Gunjan Mahindre:
Blind Image Denoising and Inpainting Using Robust Hadamard Autoencoders. CoRR abs/2101.10876 (2021) - [i11]Gunjan Mahindre, Randy C. Paffenroth, Anura P. Jayasumana, Rasika Karkare:
A Pre-training Oracle for Predicting Distances in Social Networks. CoRR abs/2106.03233 (2021) - [i10]Wenjing Li, Randy C. Paffenroth, David Berthiaume:
Neural Network Ensembles: Theory, Training, and the Importance of Explicit Diversity. CoRR abs/2109.14117 (2021) - [i9]Kelum Gajamannage, Yonggi Park, Randy C. Paffenroth, Anura P. Jayasumana:
Reconstruction of Fragmented Trajectories of Collective Motion using Hadamard Deep Autoencoders. CoRR abs/2110.10428 (2021) - [i8]Matthew L. Weiss, Nathan C. Frey, Siddharth Samsi, Randy C. Paffenroth, Vijay Gadepally:
The Pseudo Projection Operator: Applications of Deep Learning to Projection Based Filtering in Non-Trivial Frequency Regimes. CoRR abs/2111.07140 (2021) - 2020
- [j16]David Berthiaume, Randy C. Paffenroth
Understanding Deep Learning: Expected Spanning Dimension and Controlling the Flexibility of Neural Networks. Frontiers Appl. Math. Stat. 6: 572539 (2020) - [j15]Hongzhu Cui, Chong Zhou, Xinyu Dai, Yuting Liang, Randy C. Paffenroth
Boosting gene expression clustering with system-wide biological information: a robust autoencoder approach. Int. J. Comput. Biol. Drug Des. 13(1): 98-123 (2020) - [c24]Kirty Vedula, Matthew L. Weiss, Randy C. Paffenroth
Maneuvering Target Tracking using the Autoencoder-Interacting Multiple Model Filter. ACSSC 2020: 1512-1517 - [c23]Les Servi, Randy C. Paffenroth
Reducing Reporting Burden of Healthcare Data Using Robust Principal Component Analysis. IEEE BigData 2020: 3827-3836 - [c22]Nitish Bahadur, Randy C. Paffenroth
Anomaly Detection in Exchange Traded Funds. IEEE BigData 2020: 4373-4382 - [c21]Erin Teeple, Caitlin Kuhlman, Brandon Werner, Randy C. Paffenroth, Elke A. Rundensteiner:
Air Quality and Cause-specific Mortality in the United States: Association Analysis by Regression and CCA for 1980-2014. HEALTHINF 2020: 228-236 - [c20]Kirty Vedula, Randy C. Paffenroth
Joint Coding and Modulation in the Ultra-Short Blocklength Regime for Bernoulli-Gaussian Impulsive Noise Channels Using Autoencoders. ICASSP 2020: 5065-5069 - [c19]M. L. Tlachac
Topological Data Analysis to Engineer Features from Audio Signals for Depression Detection. ICMLA 2020: 302-307 - [c18]Yingnan Liu, Randy Clinton Paffenroth
Ensemble CNN in Transform Domains for Image Super-resolution from Small Data Sets. ICMLA 2020: 384-391 - [c17]Nitish Bahadur, Randy C. Paffenroth
Dimension Estimation Using Autoencoders with Applications to Financial Market Analysis. ICMLA 2020: 527-534 - [c16]Gunjan Mahindre, Rasika Karkare, Randy C. Paffenroth
Inference in Social Networks from Ultra-Sparse Distance Measurements via Pretrained Hadamard Autoencoders. LCN 2020: 256-266 - [i7]Kelum Gajamannage, Randy C. Paffenroth, Anura P. Jayasumana:
A Patch-based Image Denoising Method Using Eigenvectors of the Geodesics' Gramian Matrix. CoRR abs/2010.07769 (2020)
2010 – 2019
- 2019
- [j14]Paulo Victor Rodrigues Ferreira, Randy C. Paffenroth
Reinforcement Learning for Satellite Communications: From LEO to Deep Space Operations. IEEE Commun. Mag. 57(5): 70-75 (2019) - [j13]Mina Shojaeizadeh, Soussan Djamasbi
Detecting task demand via an eye tracking machine learning system. Decis. Support Syst. 116: 91-101 (2019) - [j12]Kelum Gajamannage
A nonlinear dimensionality reduction framework using smooth geodesics. Pattern Recognit. 87: 226-236 (2019) - [j11]Anura P. Jayasumana
Network Topology Mapping From Partial Virtual Coordinates and Graph Geodesics. IEEE/ACM Trans. Netw. 27(6): 2405-2417 (2019) - [c15]Haitao Liu, Randy C. Paffenroth
Deep Kernel Coherence Encoder. ACSSC 2019: 2067-2071 - [c14]Matthew L. Weiss, Randy C. Paffenroth
The Autoencoder-Kalman Filter: Theory and Practice. ACSSC 2019: 2176-2179 - [c13]Kelum Gajamannage
Reconstruction of Agents' Corrupted Trajectories of Collective Motion Using Low-rank Matrix Completion. IEEE BigData 2019: 2826-2834 - [c12]Nitish Bahadur, Randy C. Paffenroth
Dimenslon Estlmatlon of Equlty Markets. IEEE BigData 2019: 5491-5498 - [c11]Harsh Nilesh Pathak, Randy C. Paffenroth
Parameter Continuation Methods for the Optimization of Deep Neural Networks. ICMLA 2019: 1637-1643 - [c10]Wenjing Li, Randy C. Paffenroth
Optimal Ensembles for Deep Learning Classification: Theory and Practice. ICMLA 2019: 1658-1665 - [c9]Matthew L. Weiss, Randy C. Paffenroth
Deep Learning with Domain Randomization for Optimal Filtering. ICMLA 2019: 1779-1786 - [c8]Gunjan Mahindre, Anura P. Jayasumana, Kelum Gajamannage
On Sampling and Recovery of Topology of Directed Social Networks - A Low-Rank Matrix Completion Based Approach. LCN 2019: 324-331 - [i6]Nitish Bahadur, Randy C. Paffenroth:
Dimension Estimation Using Autoencoders. CoRR abs/1909.10702 (2019) - [i5]Kelum Gajamannage, Randy C. Paffenroth:
Bounded Manifold Completion. CoRR abs/1912.09026 (2019) - 2018
- [j10]Behnam Partopour
Random Forests for mapping and analysis of microkinetics models. Comput. Chem. Eng. 115: 286-294 (2018) - [j9]Paulo Victor Rodrigues Ferreira
Multiobjective Reinforcement Learning for Cognitive Satellite Communications Using Deep Neural Network Ensembles. IEEE J. Sel. Areas Commun. 36(5): 1030-1041 (2018) - [c7]Michael Giancola, Randy C. Paffenroth, Jacob Whitehill:
Permutation-Invariant Consensus over Crowdsourced Labels. HCOMP 2018: 21-30 - [i4]Randy C. Paffenroth, Kathleen Kay, Les Servi:
Robust PCA for Anomaly Detection in Cyber Networks. CoRR abs/1801.01571 (2018) - [i3]Anura P. Jayasumana, Randy C. Paffenroth, Gunjan Mahindre, Sridhar Ramasamy, Kelum Gajamannage:
Network Topology Mapping from Partial Virtual Coordinates and Graph Geodesics. CoRR abs/1809.03319 (2018) - [i2]Haitao Liu, Randy C. Paffenroth, Jian Zou, Chong Zhou:
Anomaly Detection via Graphical Lasso. CoRR abs/1811.04277 (2018) - 2017
- [j8]Paulo Victor Rodrigues Ferreira
Interactive Multiple Model Filter for Land-Mobile Satellite Communications at Ka-Band. IEEE Access 5: 15414-15427 (2017) - [c6]Chong Zhou, Randy C. Paffenroth
Anomaly Detection with Robust Deep Autoencoders. KDD 2017: 665-674 - [i1]Kelum Gajamannage, Randy C. Paffenroth, Erik M. Bollt:
A Nonlinear Dimensionality Reduction Framework Using Smooth Geodesics. CoRR abs/1707.06757 (2017) - 2016
- [j7]Vidarshana W. Bandara, Louis L. Scharf, Randy C. Paffenroth
Experimental recovery regions for robust PCA. Signal Process. 129: 25-32 (2016) - [j6]Tyler Reese, Randy C. Paffenroth
Duality in Geometric Graphs: Vector Graphs, Kirchhoff Graphs and Maxwell Reciprocal Figures. Symmetry 8(3): 9 (2016) - [c5]Randy C. Paffenroth
Maximum likelihood identification of an information matrix under constraints in a corresponding graphical model. ACSSC 2016: 1359-1362 - [c4]Anura P. Jayasumana, Randy C. Paffenroth
Topology maps and distance-free localization from partial virtual coordinates for IoT networks. ICC 2016: 1-6 - 2015
- [c3]Randy C. Paffenroth, Xiangnan Kong:
Python in Data Science Research and Education. SciPy 2015: 164-170 - 2013
- [j5]Randy C. Paffenroth
Space-Time Signal Processing for Distributed Pattern Detection in Sensor Networks. IEEE J. Sel. Top. Signal Process. 7(1): 38-49 (2013) - 2010
- [c2]Scott M. Lundberg, Randy C. Paffenroth, Jason Yosinski:
Analysis of CBRN sensor fusion methods. FUSION 2010: 1-8
2000 – 2009
- 2009
- [j4]Oscar P. Bruno, Tim Elling, Randy C. Paffenroth
Electromagnetic integral equations requiring small numbers of Krylov-subspace iterations. J. Comput. Phys. 228(17): 6169-6183 (2009) - 2007
- [j3]Eusebius J. Doedel, Volodymyr A. Romanov, Randy C. Paffenroth
Elemental Periodic orbits Associated with the libration Points in the Circular Restricted 3-Body Problem. Int. J. Bifurc. Chaos 17(8): 2625-2677 (2007) - 2003
- [j2]Eusebius J. Doedel, Randy C. Paffenroth
Computation of Periodic Solutions of Conservative Systems with Application to the 3-body Problem. Int. J. Bifurc. Chaos 13(6): 1353-1381 (2003) - 2002
- [j1]Kathleen A. Hoffman, Robert S. Manning
Calculation of the Stability Index in Parameter-Dependent Calculus of Variations Problems: Buckling of a Twisted Elastic Strut. SIAM J. Appl. Dyn. Syst. 1(1): 115-145 (2002)
1990 – 1999
- 1994
- [c1]Gábor Domokos, Randy C. Paffenroth:
A Case Study on Visualization for Boundary Value Problems. IEEE Visualization 1994: 345-348
Coauthor Index
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