When learning a mixture model, we suffer from the local optima and model structure determination problems. In this paper, we present a method for simultaneously solving these prob...
We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the ...
Feiping Nie, Dong Xu, Ivor Wai-Hung Tsang, Changsh...
—This paper presents a volumetric modeling framework to construct a novel spline scheme called restricted trivariate polycube splines (RTP-splines). The RTP-spline aims to genera...
Kexiang Wang, Xin Li, Bo Li 0014, Huanhuan Xu, Hon...
Abstract. We introduce a mathematical framework for black-box software testing of functional correctness, based on concepts from stochastic process theory. This framework supports ...
We give new proofs of soundness (all representable functions on base types lies in certain complexity classes) for Light Affine Logic, Elementary Affine Logic, LFPL and Soft Af...