Feb 24, 2018 · Our experiments show that GraphRNN significantly outperforms all baselines, learning to generate diverse graphs that match the structural characteristics of a target set, while also scaling to graphs 50 times larger than previous deep models.

Classify images by taking a series of “glimpses”. Ba, Mnih, and Kavukcuoglu, “Multiple Object Recognition with Visual Attention”, ICLR 2015. Gregor et al, “DRAW: A Recurrent Neural Network For Image Generation”, ICML 2015.

Jul 11, 2019 · The main objective of this post is to implement an RNN from scratch and provide an easy explanation as well to make it useful for the readers. Implementing any neural network from scratch at least ...

We define GRNN as particular cases of RNN in which the signals at each point in time are supported on a graph. In this lecture we will present how to construct a GRNN, going over each part of the architecture in detail.

Jul 21, 2022 · Learn everything about Graph Neural Networks, including what GNNs are, the different types of graph neural networks, and what they're used for. Plus, learn how to build a Graph Neural Network with Pytorch.

Architecture of a traditional RNN Recurrent neural networks, also known as RNNs, are a class of neural networks that allow previous outputs to be used as inputs while having hidden states. They are typically as follows:

An RNN is unrolled by expanding its computation graph over time, effectively "removing" the cyclic connections. This is done by capturing the state of the entire RNN (called a slice) at each time instant \(t\) and treating it similar to how layers are treated in feedforward neural networks.

Training “Feedforward” Neural Networks. One time set up: activation functions, preprocessing, weight initialization, regularization, gradient checking. Training dynamics: babysitting the learning process, parameter updates, hyperparameter optimization.

Applying neural architecture search (NAS) to a large dataset like ImageNet is expensive. Design a search space of building blocks (“cells”) that can be flexibly stacked. NASNet: Use NAS to find best cell structure on smaller CIFAR-10 dataset, then transfer architecture to ImageNet.

Our experiments show that GraphRNN significantly outperforms all baselines, learning to generate diverse graphs that match the structural characteristics of a target set, while also scaling to graphs 50 times larger than previous deep models.