based on convolutions with either the normalised sampled Gaussian kernel or the integrated Gaussian kernel followed by central differences. The motivation for studying these discretisation methods is ...

To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal ...

The function takes a vector of K real numbers as input and returns x * sigmoid(1.702*x).

We propose a spherical kernel for efficient graph convolution of 3D point clouds ... Install Tensorflow. The code was tested with Python 3.5, Tensorflow 1.12.0, Cuda 9.0 and Cudnn 7.1.4 on Ubuntu ...

Abstract: Gaussian processes (GPs) stand as crucial tools in machine learning and signal processing, with their effectiveness hinging on kernel design and hyperparameter optimization. This article ...

This study evaluates the effectiveness of the multi-task Gaussian process (MTGP) based on the linear model of coregionalization (LMC) for imputing missing daily rainfall data in Burkina Faso, ...

What do you wonder? By The Learning Network A new collection of graphs, maps and charts organized by topic and type from our “What’s Going On in This Graph?” feature. By The Learning ...

The Graph price prediction anticipates a high of $0.419 by the end of 2025. In 2028, it will range between $0.978 and $1.12, with an average price of $1.05. In 2031, it will range between $1.68 and $1 ...

They require a knowledge graph. How does the journey to a knowledge graph start with unstructured data—such as text, images, and other media? The evolution of web search engines offers an ...