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1. How Deep Are Deep Gaussian Processes?

   Dunlop, Matthew M. ( September 2018 , Journal of machine learning research)

   Lawrence, Neil (Ed.)

   Recent research has shown the potential utility of deep Gaussian processes. These deep structures are probability distributions, designed through hierarchical construction, which are conditionally Gaussian. In this paper, the current published body of work is placed in a common framework and, through recursion, several classes of deep Gaussian processes are defined. The resulting samples generated from a deep Gaussian process have a Markovian structure with respect to the depth parameter, and the effective depth of the resulting process is interpreted in terms of the ergodicity, or non-ergodicity, of the resulting Markov chain. For the classes of deep Gaussian processes introduced, we provide results concerning their ergodicity and hence their effective depth. We also demonstrate how these processes may be used for inference; in particular we show how a Metropolis-within-Gibbs construc-tion across the levels of the hierarchy can be used to derive sampling tools which are robust to the level of resolution used to represent the functions on a computer. For illustration, we consider the effect of ergodicity in some simple numerical examples. 

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2. Deep RC: Enabling Remote Control through Deep Learning

   https://doi.org/10.1109/ICUAS.2019.8798325  

   Ellingson, Jaron ; Ellingson, Gary ; McLain, Tim ( June 2019 , International Conference on Unmanned Aircraft Systems)

   Human remote-control (RC) pilots have the ability to perceive the position and orientation of an aircraft using only third-person-perspective visual sensing. While novice pilots often struggle when learning to control RC aircraft, they can sense the orientation of the aircraft with relative ease. In this paper, we hypothesize and demonstrate that deep learning methods can be used to mimic the human ability to perceive the orientation of an aircraft from monocular imagery. This work uses a neural network to directly sense the aircraft attitude. The network is combined with more conventional image processing methods for visual tracking of the aircraft. The aircraft track and attitude measurements from the convolutional neural network (CNN) are combined in a particle filter that provides a complete state estimate of the aircraft. The network topology, training, and testing results are presented as well as filter development and results. The proposed method was tested in simulation and hardware flight demonstrations. 

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3. Deep Knockoffs

   https://doi.org/10.1080/01621459.2019.1660174  

   Romano, Yaniv ; Sesia, Matteo ; Candès, Emmanuel ( October 2020 , Journal of the American Statistical Association)

   null (Ed.)
4. Deep AutoAugment

   Zheng, Yu ; Zhang, Zhi ; Yan, Shen ; Zhang, Mi ( January 2022 , International Conference on Learning Representations (ICLR))
5. Deep GUI: Black-box GUI Input Generation with Deep Learning

   https://doi.org/10.1109/ASE51524.2021.9678778  

   YazdaniBanafsheDaragh, Faraz ; Malek, Sam ( November 2021 , Proceedings of the 36th IEEE/ACM International Conference on Automated Software Engineering (ASE 2021))