This paper proposes a hybrid model for deformable template which combines alignable and non-alignable sketches. These sketches are subject to slight or considerable translations i...
In this paper we propose a genetic programming approach to learning stochastic models with unsymmetrical noise distributions. Most learning algorithms try to learn from noisy data...
Policy gradient (PG) reinforcement learning algorithms have strong (local) convergence guarantees, but their learning performance is typically limited by a large variance in the e...
Recent work has exploited boundedness of data in the unsupervised learning of new types of generative model. For nonnegative data it was recently shown that the maximum-entropy ge...
Recently, the maximum likelihood estimator (MLE) and Cramer-Rao Lower Bound (CRLB) were proposed with the goal of maximizing and assessing the synchronization accuracy in wireless...
Jang-Sub Kim, Jaehan Lee, Erchin Serpedin, Khalid ...