| Neural network-based learning from demonstration of an autonomous ground robot Y Fu, DK Jha, Z Zhang, Z Yuan, A Ray Machines 7 (2), 24, 2019 | 16 | 2019 |
| Neural probabilistic forecasting of symbolic sequences with long short-term memory M Hauser, Y Fu, S Phoha, A Ray Journal of Dynamic Systems, Measurement, and Control 140 (8), 2018 | 9 | 2018 |
| ROPNN: Detection of ROP payloads using deep neural networks X Li, Z Hu, Y Fu, P Chen, M Zhu, P Liu arXiv preprint arXiv:1807.11110, 2018 | 8 | 2018 |
| A Dynamically Stabilized Recurrent Neural Network S Saab, Y Fu, A Ray, M Hauser Neural Processing Letters, 1-15, 2021 | 5 | 2021 |
| A dynamically controlled recurrent neural network for modeling dynamical systems Y Fu, S Saab Jr, A Ray, M Hauser arXiv preprint arXiv:1911.00089, 2019 | 5 | 2019 |
| Bayesian nonparametric modeling of categorical data for information fusion and causal inference S Xiong, Y Fu, A Ray Entropy 20 (6), 396, 2018 | 5 | 2018 |
| Bayesian nonparametric regression modeling of panel data for sequential classification S Xiong, Y Fu, A Ray IEEE Transactions on Neural Networks and Learning Systems 29 (9), 4128-4139, 2017 | 4 | 2017 |
| Mad: Self-supervised masked anomaly detection task for multivariate time series Y Fu, F Xue 2022 International Joint Conference on Neural Networks (IJCNN), 1-8, 2022 | 3 | 2022 |
| DeepReturn: a deep neural network can learn how to detect previously-unseen rop payloads without using any heuristics X Li, Z Hu, H Wang, Y Fu, P Chen, M Zhu, P Liu Journal of Computer Security 28 (5), 499-523, 2020 | 3 | 2020 |
| Probabilistic forecasting of symbol sequences with deep neural networks M Hauser, Y Fu, Y Li, S Phoha, A Ray 2017 American Control Conference (ACC), 3147-3152, 2017 | 3 | 2017 |
| Spatiotemporal representation learning with gan trained lstm-lstm networks Y Fu, S Sen, J Reimann, C Theurer 2020 IEEE International Conference on Robotics and Automation (ICRA), 10548 …, 2020 | 2 | 2020 |
| Backpropagation through time and space: learning numerical methods with multi-agent reinforcement learning E Way, DSK Kapilivai, Y Fu, L Yu arXiv preprint arXiv:2203.08937, 2022 | 1 | 2022 |
| Multi-agent Learning of Numerical Methods for Hyperbolic PDEs with Factored Dec-MDP Y Fu, DSK Kapilavai, E Way Advances in Practical Applications of Agents, Multi-Agent Systems, and …, 2022 | | 2022 |
| Masked Multi-Step Multivariate Time Series Forecasting with Future Information Y Fu, H Wang, N Virani arXiv preprint arXiv:2209.14413, 2022 | | 2022 |
| Deep Analysis Net with Causal Embedding for Coal-fired power plant Fault Detection and Diagnosis (DANCE4CFDD) F Xue, H Huang, Y Fu, B Feng, W Yan, T Wang GE Research, 2022 | | 2022 |
| Trajectory Planning for Aerospace Vehicles using Deep Reinforcement Learning. D Kapilavai, S Roychowdhury, Y Fu, L Yu, BG van Bloemen Waanders Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2021 | | 2021 |
| Modeling, Prediction and Control of Engineering Systems with Long Short-Term Memory Y Fu The Pennsylvania State University, 2020 | | 2020 |
