GPU Performance Background User's Guide - NVIDIA Docs
WEBFeb 1, 2023 · Learn how GPUs execute operations in parallel and how to estimate performance limitations with arithmetic intensity. Understand the structure, execution model, and memory bandwidth of NVIDIA GPUs for deep learning applications.
Pooling layer - Wikipedia
WEBPooling is most commonly used in convolutional neural networks (CNN). Below is a description of pooling in 2-dimensional CNNs. The generalization to n-dimensions is immediate. As notation, we consider a tensor , where is height, is width, and is the number of channels. A pooling layer outputs a tensor .
Graph pooling in graph neural networks: methods and their
WEBSep 16, 2024 · Some pooling methods, such as edge contraction pooling (Diehl et al. 2019), and selection pooling (Bianchi et al. 2022b), achieve a fixed pooling ratio, generating a coarsened graph with 50% of the nodes at a time.
WEBGraph pooling is a technique to reduce the size of graphs in graph neural networks. This paper proposes a formal framework to characterize and classify graph pooling operators based on three functions: selection, reduction, and connection.
torch_geometric.nn.pool.TopKPooling
WEBLearn how to use TopKPooling, a module that performs top k pooling on graph nodes based on their scores. See the parameters, equations and examples of this operator from the pytorch_geometric documentation.
torch_geometric.nn.pool.ASAPooling
WEBratio (float or int) – Graph pooling ratio, which is used to compute \(k = \lceil \mathrm{ratio} \cdot N \rceil\), or the value of \(k\) itself, depending on whether the type of ratio is float or int.
Understanding Pooling in Graph Neural Networks - IEEE Xplore
WEBJul 21, 2022 · A framework and a taxonomy to unify various pooling operators in graph machine learning, based on selection, reduction, and connection functions. The article also proposes criteria to evaluate the performance of pooling operators and applies them to …
[2110.05292] Understanding Pooling in Graph Neural Networks
WEBOct 11, 2021 · In this paper we propose a formal characterization of graph pooling based on three main operations, called selection, reduction, and connection, with the goal of unifying the literature under a common framework.
[2204.07321] Graph Pooling for Graph Neural Networks: Progress ...
WEBApr 15, 2022 · A comprehensive survey of graph pooling methods for graph-level tasks, such as graph classification and graph generation. The paper proposes a taxonomy, summarizes existing libraries, applications, and future directions for …
(PDF) Understanding Pooling in Graph Neural Networks
WEBJul 21, 2022 · In this article, we present an operational framework to unify this vast and diverse literature by describing pooling operators as the combination of three functions: selection, reduction, and...