We propose a new transform coding algorithm that integrates all optimization steps into a coherent and consistent framework. Each iteration of the algorithm is designed to minimiz...
Supervised learning deals with the inference of a distribution over an output or label space $\CY$ conditioned on points in an observation space $\CX$, given a training dataset $D$...
In this paper we propose a novel analog design optimization methodology to address two key aspects of top-down system-level design: (1) how to optimally compare and select analog ...
Xin Li, Jian Wang, Lawrence T. Pileggi, Tun-Shih C...
Many vision applications have been formulated as Markov Random Field (MRF) problems. Although many of them are discrete labeling problems, continuous formulation often achieves gre...
Wonsik Kim (Seoul National University), Kyoung Mu ...
— In this paper, we propose a new algorithm, named JACC-G, for large scale optimization problems. The motivation is to improve our previous work on grouping and adaptive weightin...
Zhenyu Yang, Jingqiao Zhang, Ke Tang, Xin Yao, Art...