Zobrazeno 1 - 10
of 108
pro vyhledávání: '"Liao, Xuejun"'
We present a probabilistic framework for nonlinearities, based on doubly truncated Gaussian distributions. By setting the truncation points appropriately, we are able to generate various types of nonlinearities within a unified framework, including s
Externí odkaz:
http://arxiv.org/abs/1709.06123
Gaussian graphical models (GGMs) are widely used for statistical modeling, because of ease of inference and the ubiquitous use of the normal distribution in practical approximations. However, they are also known for their limited modeling abilities,
Externí odkaz:
http://arxiv.org/abs/1611.04920
We introduce the truncated Gaussian graphical model (TGGM) as a novel framework for designing statistical models for nonlinear learning. A TGGM is a Gaussian graphical model (GGM) with a subset of variables truncated to be nonnegative. The truncated
Externí odkaz:
http://arxiv.org/abs/1606.00906
Since the spectrogram does not preserve phase information contained in the original data, any algorithm based on the spectrogram is not likely to be optimum for detection. In this paper, we present the Short Time Fourier Transform detector to detect
Externí odkaz:
http://arxiv.org/abs/1510.05520
We utilize copulas to constitute a unified framework for constructing and optimizing variational proposals in hierarchical Bayesian models. For models with continuous and non-Gaussian hidden variables, we propose a semiparametric and automated variat
Externí odkaz:
http://arxiv.org/abs/1506.05860
Expectation maximization (EM) has recently been shown to be an efficient algorithm for learning finite-state controllers (FSCs) in large decentralized POMDPs (Dec-POMDPs). However, current methods use fixed-size FSCs and often converge to maxima that
Externí odkaz:
http://arxiv.org/abs/1505.00274
Autor:
Yuan, Xin, Llull, Patrick, Liao, Xuejun, Yang, Jianbo, Sapiro, Guillermo, Brady, David J., Carin, Lawrence
A simple and inexpensive (low-power and low-bandwidth) modification is made to a conventional off-the-shelf color video camera, from which we recover {multiple} color frames for each of the original measured frames, and each of the recovered frames c
Externí odkaz:
http://arxiv.org/abs/1402.6932
Autor:
Yuan, Xin, Yang, Jianbo, Llull, Patrick, Liao, Xuejun, Sapiro, Guillermo, Brady, David J., Carin, Lawrence
This paper introduces the concept of adaptive temporal compressive sensing (CS) for video. We propose a CS algorithm to adapt the compression ratio based on the scene's temporal complexity, computed from the compressed data, without compromising the
Externí odkaz:
http://arxiv.org/abs/1302.3446
Autor:
Llull, Patrick, Liao, Xuejun, Yuan, Xin, Yang, Jianbo, Kittle, David, Carin, Lawrence, Sapiro, Guillermo, Brady, David J.
We use mechanical translation of a coded aperture for code division multiple access compression of video. We present experimental results for reconstruction at 148 frames per coded snapshot.
Comment: 19 pages (when compiled with Optics Express'
Comment: 19 pages (when compiled with Optics Express'
Externí odkaz:
http://arxiv.org/abs/1302.2575
Learning multiple tasks across heterogeneous domains is a challenging problem since the feature space may not be the same for different tasks. We assume the data in multiple tasks are generated from a latent common domain via sparse domain transforms
Externí odkaz:
http://arxiv.org/abs/1206.6419