Graph Neural Networks (GNNs) has seen rapid development lately with a good number of research papers published at recent conferences. I am putting together a short intro of GNN and a summary of the latest research talks.
Graph Neural Networks (GNNs) has seen rapid development lately with a good number of research papers published at recent conferences. I am putting together a short intro of GNN and a summary of the latest research talks.Hope it is helpful for anyone who are getting into the field or trying to catch up the updates.
Graph Neural Networks are neural networks that operate on graph data. This informative intro to GNNs defines them as “an optimizable transformation on all attributes of the graph (nodes, edges, global-context) that preserves graph symmetries (permutation invariances).”
Graph Neural Networks BGCF is a recommendation method based on the Bayesian graph neural network, where the user-item interaction is viewed as a bipartite graph. Furthermore, the similarities between users and the commonalities of items can be explicitly modeled as user-user and item-item graphs, respectively.
Xamarin is an open-source platform for building applications using C# and .NET. This is likely to aid developers in building AI models over Android or iOS platforms. This new release enables the building of cross-platform applications using Xamarin.Forms. Microsoft has also added an example application in Xamarin, which runs a ResNet classifier ...
πππππ : GANs for time-series data where CNNs were replaced with RNNs (recurrent neural networks) to accommodate for the nature of this type of data. ππ’π¦ππππ : another time-series GAN where new techniques were introduced such as a stepwise supervised loss and an autoencoder.
The questions is on what tasks and on what graph types are simpler methods SOTA and on which graphs/tasks are huge/deep/high-order methods actually better. However, people in the GNN community are quite aware of this, and there have been recent efforts to improve benchmarking, namely OGB and "Benchmarking Graph Neural Networks".
I'm only lukewarm on Graph Neural Networks (GNNs). There, I said it. It might sound crazy GNNs are one of the hottest fields in machine learning right now. There were at least four review papers just in the last few months. I think some progress can come of this research, but we're also focusing on some incorrect places.
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Here is an example: Let's say I have a mesh that contains input and output nodes, each node has some type of interaction with its connections and it is a directed, and perhaps if needed acyclic graph. The interactions could be like if both of my inputs are + then my left output is + and right -, otherwise both are -.
Here is an example: Let's say I have a mesh that contains input and output nodes, each node has some type of interaction with its connections and it is a directed, and perhaps if needed acyclic graph. The interactions could be like if both of my inputs are + then my left output is + and right -, otherwise both are -.