| 2025 | ACSSC | Physically Constrained Adversarial Examples for Fine-Tuning Trajectory Classifiers. | Simeon Krastev, Randy C. Paffenroth, Olivia Cava |
| 2025 | ICMLA | Iterative Denoising Autoencoders with Adaptive Iterations Trained Using Sum Iteration Losses. | Shiquan He, Randy C. Paffenroth |
| 2025 | ICMLA | Sparse RNNs in Reinforcement Learning Applications. | Quincy Hershey, Randy C. Paffenroth |
| 2025 | ICMLA | Unitary Vision: Deep Learning with Information Decorrelation for Signal Reconstruction. | Steven Tate, Randy C. Paffenroth |
| 2024 | ICMLA | Oracle Embeddings for Chemical Detection. | Cate Dunham, Maria Barger, Randy C. Paffenroth, Joshua R. Uzarski, Chia-Wei Tsai |
| 2024 | ICMLA | Dynamical System Autoencoders. | Shiquan He, Randy C. Paffenroth, Olivia Cava, Cate Dunham |
| 2024 | ICMLA | Virtual-Coordinate Based Sampling and Embedding for Machine Learning with Graph Data. | Zheyi Qin, Anura P. Jayasumana, Randy C. Paffenroth |
| 2023 | ACSSC | Enhancing Neural Network Performance for Problems in the Physical Sciences: Applications to Electromagnetic Signal Source Localization. | Maria Barger, Evan C. Witz, Randy C. Paffenroth |
| 2023 | ICCCN | One-class Classification Using Autoencoder Feature Residuals for Improved IoT Network Intrusion Detection. | Brian Lewandowski, Randy C. Paffenroth |
| 2023 | ICMLA | Overwater Emitter Localization with a Single Receiver using Neural Network Modeling. | Maria Barger, Patrick Bidigare, D. Richard Brown, Ilana Heintz, Randy C. Paffenroth, Evan C. Witz |
| 2023 | ICMLA | Conditioned Cycles in Sparse Data Domains: Applications to Electromagnetics. | Maria Barger, Randy C. Paffenroth, Harsh Nilesh Pathak |
| 2023 | ICMLA | Exploring Neural Network Structure through Sparse Recurrent Neural Networks: A Recasting and Distillation of Neural Network Hyperparameters. | Quincy Hershey, Randy C. Paffenroth, Harsh Nilesh Pathak |
| 2023 | ICMLA | ChemVise: Maximizing Out-of-Distribution Chemical Detection with a Novel Application of Transfer Learning. | Alexander M. Moore, Randy C. Paffenroth, Ken T. Ngo, Joshua R. Uzarski |
| 2023 | ICMLA | Sequentia12D: Organizing Center of Skip Connections for Transformers. | Harsh Nilesh Pathak, Randy C. Paffenroth, Quincy Hershey |
| 2022 | ICMLA | Autoencoder Feature Residuals for Network Intrusion Detection: Unsupervised Pre-training for Improved Performance. | Brian Lewandowski, Randy C. Paffenroth |
| 2022 | ICMLA | ACGANs Improve Chemical Sensors for Challenging Distributions. | Alexander M. Moore, Randy C. Paffenroth, Ken T. Ngo, Joshua R. Uzarski |
| 2021 | ICMLA | Theory for Deep Learning Regression Ensembles with Application to Raman Spectroscopy Analysis. | Wenjing Li, Randy C. Paffenroth, Michael T. Timko, Matthew P. Rando, Avery B. Brown, N. Aaron Deskins |
| 2021 | ICMLA | Deep Learning for Range Localization via Over-Water Electromagnetic Signals. | Evan C. Witz, Maria Barger, Randy C. Paffenroth |
| 2020 | ACSSC | Maneuvering Target Tracking using the Autoencoder-Interacting Multiple Model Filter. | Kirty Vedula, Matthew L. Weiss, Randy C. Paffenroth, Joshua R. Uzarski, D. Richard Brown |
| 2020 | ICASSP | Joint Coding and Modulation in the Ultra-Short Blocklength Regime for Bernoulli-Gaussian Impulsive Noise Channels Using Autoencoders. | Kirty Vedula, Randy C. Paffenroth, D. Richard Brown |
| 2020 | ICMLA | Dimension Estimation Using Autoencoders with Applications to Financial Market Analysis. | Nitish Bahadur, Randy C. Paffenroth |
| 2020 | ICMLA | Topological Data Analysis to Engineer Features from Audio Signals for Depression Detection. | M. L. Tlachac, Adam Sargent, Ermal Toto, Randy C. Paffenroth, Elke A. Rundensteiner |
| 2020 | LCN | Inference in Social Networks from Ultra-Sparse Distance Measurements via Pretrained Hadamard Autoencoders. | Gunjan Mahindre, Rasika Karkare, Randy C. Paffenroth, Anura P. Jayasumana |
| 2019 | ACSSC | Deep Kernel Coherence Encoder. | Haitao Liu, Randy C. Paffenroth, Louis Scharf, Fangzheng Sun |
| 2019 | ACSSC | The Autoencoder-Kalman Filter: Theory and Practice. | Matthew L. Weiss, Randy C. Paffenroth, Joshua R. Uzarski |
| 2019 | ICMLA | Optimal Ensembles for Deep Learning Classification: Theory and Practice. | Wenjing Li, Randy C. Paffenroth |
| 2019 | ICMLA | Parameter Continuation Methods for the Optimization of Deep Neural Networks. | Harsh Nilesh Pathak, Randy C. Paffenroth |
| 2019 | ICMLA | Deep Learning with Domain Randomization for Optimal Filtering. | Matthew L. Weiss, Randy C. Paffenroth, Jacob Whitehill, Joshua R. Uzarski |
| 2019 | LCN | On Sampling and Recovery of Topology of Directed Social Networks - A Low-Rank Matrix Completion Based Approach. | Gunjan Mahindre, Anura P. Jayasumana, Kelum Gajamannage, Randy C. Paffenroth |
| 2018 | HCOMP | Permutation-Invariant Consensus over Crowdsourced Labels. | Michael Giancola, Randy C. Paffenroth, Jacob Whitehill |
| 2017 | KDD | Anomaly Detection with Robust Deep Autoencoders. | Chong Zhou, Randy C. Paffenroth |
| 2016 | ACSSC | Maximum likelihood identification of an information matrix under constraints in a corresponding graphical model. | Randy C. Paffenroth, Nan Li, Louis L. Scharf |
| 2010 | FUSION | Analysis of CBRN sensor fusion methods. | Scott M. Lundberg, Randy C. Paffenroth, Jason Yosinski |