Existing Recurrent Neural Networks (RNNs) are limited in their ability to model dynamical systems with nonlinearities and hidden internal states. Here we use our general framework...
Abstract. The problem we study in this paper is the key recovery problem on the C schemes and generalizations where the quadratic monomial of C (the product of two linear monomials...
Pierre-Alain Fouque, Gilles Macario-Rat, Jacques S...
Kernel Principal Component Analysis (KPCA) is a popular generalization of linear PCA that allows non-linear feature extraction. In KPCA, data in the input space is mapped to highe...
This paper presents a technique for computing the geometry of objects with general reflectance properties from images. For surfaces with varying material properties, a full segmen...
Helmholtz stereopsis guarantees unbiasedness by BRDF of the search for inter-image correspondences. In a practical setup, calibrated pixel sensitivity and corrected light anisotro...