1 code implementation • 22 May 2023 • Shuoyang Wang, Guanqun Cao
The intrinsically infinite-dimensional features of the functional observations over multidimensional domains render the standard classification methods effectively inapplicable.
1 code implementation • 19 May 2022 • Shuoyang Wang, Guanqun Cao
For any multi-dimensional functional data, we provide the uniform convergence rates for the proposed robust deep neural networks estimators.
1 code implementation • 17 May 2022 • Shuoyang Wang, Guanqun Cao, Zuofeng Shang
We propose a new approach, called as functional deep neural network (FDNN), for classifying multi-dimensional functional data.
no code implementations • 28 Dec 2021 • Guanqun Cao, Shan Luo
Due to the complementary properties between visual and tactile senses, it is desirable for us to combine vision and touch for a synergistic perception and manipulation.
no code implementations • 13 May 2021 • Jiaqi Jiang, Guanqun Cao, Daniel Fernandes Gomes, Shan Luo
In recent years, computer vision techniques have been applied in detecting cracks in concrete structures.
no code implementations • 8 Dec 2020 • Shuoyang Wang, Guanqun Cao, Zuofeng Shang
In this work, we propose a deep neural network method to perform nonparametric regression for functional data.
no code implementations • 10 Aug 2020 • Guanqun Cao, Yi Zhou, Danushka Bollegala, Shan Luo
Recently, tactile sensing has attracted great interest in robotics, especially for facilitating exploration of unstructured environments and effective manipulation.
no code implementations • 31 Jan 2018 • Guanqun Cao, Alexandros Iosifidis, Moncef Gabbouj, Vijay Raghavan, Raju Gottumukkala
We study the problem of learning to rank from multiple information sources.
no code implementations • 31 Aug 2017 • Guanqun Cao, Alexandros Iosifidis, Moncef Gabbouj
Face verification is a problem approached in the literature mainly using nonlinear class-specific subspace learning techniques.
no code implementations • 31 May 2016 • Guanqun Cao, Alexandros Iosifidis, Ke Chen, Moncef Gabbouj
In this paper, the problem of multi-view embedding from different visual cues and modalities is considered.