… In this work, we explore one such architecture class, called Transformation Networks (TN) …
of networks can be built called Non-Parametric Transformation Networks (NPTNs). NPTNs …

… In this work, we explore one such architecture class, called Transformation Networks (TN) …
of networks can be built called Non-Parametric Transformation Networks (NPTNs). NPTNs …

… Although deep convolutional networks can have large receptive fields, our experiments …
affine network does not perform well in practice. Instead, we compose the affine transformation …

… A Bayesian network (BN) is a probabilistic graphical model that provides an elegant way of
… This paper concentrates only on one method called non-parametric BNs (NPBNs). NPBNs …

… ., 2018), where networks evolve over time. In this paper we develop some non-parametric
regression methods for modelling and predicting networks where covariates are available. An …

… Mvtn: Multi-view transformation network for 3d shape recognition. In Proceedings of the
IEEE/CVF International Conference on Computer Vision, pages 1–11, 2021. 12 [23] Chenhang …

… us to make an efficient non-parametric implementation and requires no prior … transforms,
we also discuss nonlinear transforms that are implemented as radial basis function networks…

… a result, non-parametric tools are required. This study compares parametric and non-parametric …
To compare the methods, the unscented transform and two-point estimation approaches …

… encoder conducts initial embedding to transform the raw XYZ coordinates of P into high-dimensional
vectors, and progressively aggregates local patterns via the multi-stage hierarchy. …

… by using two nonparametric approaches, ie, the neural-network and the statistical …
network behaviour, two transformations (which save the inputoutput response of the network…