所谓的computational graph意思就是说这个graph他是一种语言,这个语言是用来描述一个function用的,我们知道neural network就是一个function,所以我们需要描述function的语言。computational graph就是一种语言,你可以看着他的graph以后,一眼就可以看出来我们要描述的function长什么样,其实graph有很多种定义方法,但是我们通常使...
因为我们要做neural network的时候,我们需要计算偏微分的对象其实是cost function(loss function),我们要对cost function算偏微分,而cost function的输出就是一个scalar,所以如果你把cost function用computational graph来描述的话,他只有一个输出,那今天我们又要同时算这个network里面所有参数对cost function的偏微分,所以...
The present invention discloses a method to optimize a neural network computational graph. The computational graph is used for performing neural network calculation by a computational platform. The computational platform reads data needed by the calculation from off-chip memory. The method comprises: ...
Differentnetwork architecturescan be used to solve different problems and model different neural systems. In principle a neural network can solve any computational problem to any specified accuracy, though designing procedures for learning appropriate weights (e.g., by gradient descent) may be more inv...
For example, there have been successes in achieving bioinspired hierarchical composites, in using semi-supervised approaches with graph neural networks, and in implementing natural language inputs for generative design of architected materials. Concurrently, machine learning (ML) models have been used in ...
Computational Graphs - Backpropagation is implemented in deep learning frameworks like Tensorflow, Torch, Theano, etc., by using computational graphs. More significantly, understanding back propagation on computational graphs combines several different a
Here we assess the applicability of graph neural networks (GNNs) for predicting the grain-scale elastic response of polycrystalline metallic alloys. Using GNN surrogate models, grain-averaged stresses during uniaxial elastic tension in low solvus high-re
(nm–μm), shape, orientation, and adjacency relation of the grains. Here, we develop a graph neural network1,2based machine learning model which enables an accurate prediction of the property of polycrystalline microstructures and quantifying the relative importance of each feature in each grain ...
For example, there have been successes in achieving bioinspired hierarchical composites, in using semi-supervised approaches with graph neural networks, and in implementing natural language inputs for generative design of architected materials. Concurrently, machine learning (ML) models have been used in...
domain (i.e., vertex domain) as the aggregations of node representations from the node neighborhoods. The emergence of these operations opens a door to graph convolutional networks. Generally speaking, graph convolutional network models are a type of neural network architectures that can leverage the...