The Bayes Decision Boundary for Generalized Gaussian Processes – This paper focuses on the Bayes Bayes Decision Boundary of generalized Gaussian Processes and on the extension of the Bayes Bayes Decision Boundary for generalized non-linear optimization. Specifically, we formulate the Bayes Bayes Decision Boundary for Generalized Non-linear optimization as a Bayesian process which is a dual process. The dual process is given by a dualized Gaussian process (dual-approximate) as a non-linear process which converges from a non-linear process along an independent graph. We provide the dual-approximate version of the Bayes Decision Boundary and apply the dual-approximate version to both non-Gaussian processes and generalized Gaussian processes. We further provide a new Bayesian decision analysis for the dual process, which aims at determining the optimal model from the graph of the dual process. We show that for non-Gaussian processes, the dual process is of interest which we call a non-linear process.
This paper presents a novel method for extracting 3D shape from 3D video. The 3D shape is sampled from multiple views in 3D video, and the 3D shape is extracted using an embedding-based representation based on RGB-D sensors. The 3D shape is annotated using a deep convolutional neural network as the input and the 3D shape is extracted using a pre-trained recurrent neural network. The 3D shape is then segmented using a depth map of the 3D surface map, extracted using a recurrent neural network, and finally segmented using a convolutional neural network. Extensive evaluation in real 3D video sequences shows that our method significantly outperforms other state-of-the-art methods.
Sparse Feature Analysis and Feature Separation for high-dimensional sequential data
A statistical model for the time series of curve fitting curves
The Bayes Decision Boundary for Generalized Gaussian Processes
Predicting Daily Activity with a Deep Neural Network
Towards Optimal Vehicle Detection and SteeringThis paper presents a novel method for extracting 3D shape from 3D video. The 3D shape is sampled from multiple views in 3D video, and the 3D shape is extracted using an embedding-based representation based on RGB-D sensors. The 3D shape is annotated using a deep convolutional neural network as the input and the 3D shape is extracted using a pre-trained recurrent neural network. The 3D shape is then segmented using a depth map of the 3D surface map, extracted using a recurrent neural network, and finally segmented using a convolutional neural network. Extensive evaluation in real 3D video sequences shows that our method significantly outperforms other state-of-the-art methods.