Yiwei Fu
Cited by
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A Dynamically Stabilized Recurrent Neural Network
S Saab, Y Fu, A Ray, M Hauser
Neural Processing Letters, 1-15, 2021
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
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), 084502, 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
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
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
Bayesian nonparametric modeling of categorical data for information fusion and causal inference
S Xiong, Y Fu, A Ray
Entropy 20 (6), 396, 2018
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
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
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
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
Masked Multi-Step Multivariate Time Series Forecasting with Future Information
Y Fu, H Wang, N Virani
arXiv preprint arXiv:2209.14413, 2022
Backpropagation through time and space: learning numerical methods with multi-agent reinforcement learning
E Way, DSK Kapilavai, Y Fu, L Yu
arXiv preprint arXiv:2203.08937, 2022
Probabilistic and Semantic Descriptions of Image Manifolds and Their Applications
P Tu, Z Yang, R Hartley, Z Xu, J Zhang, D Campbell, J Singh, T Wang
arXiv preprint arXiv:2307.02881, 2023
Masked Multi-Step Probabilistic Forecasting for Short-to-Mid-Term Electricity Demand
Y Fu, N Virani, H Wang
arXiv preprint arXiv:2302.06818, 2023
Multi-agent Learning of Numerical Methods for Hyperbolic PDEs with Factored Dec-MDP
Y Fu, DSK Kapilavai, E Way
International Conference on Practical Applications of Agents and Multi-Agent …, 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
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
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