Principal differential analysis (PDA) is an alternative parameter estimation technique for differential equation models in which basis functions (e.g., B-splines) are fitted to dy...
A. A. Poyton, M. S. Varziri, K. B. McAuley, P. J. ...
In this paper, we explore a privacy algorithm that detects human private parts in a 3D scan data set. The analogia graph is introduced to study the proportion of structures. The i...
This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
In recent years, gradient-based LSTM recurrent neural networks (RNNs) solved many previously RNN-unlearnable tasks. Sometimes, however, gradient information is of little use for t...
Particle filtering provides a general framework for propagating probability density functions in non-linear and non-Gaussian systems. However, the algorithm is based on a Monte Ca...
Bohyung Han, Ying Zhu, Dorin Comaniciu, Larry S. D...