Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature. These models learn a message passing algorithm and aggregation procedure to compute a function of their entire input graph. At this point, the next step is to find a particularly effective variant of this general approach and apply it to chemical prediction benchmarks until we either solve them or reach the limits of the approach. In this paper, we reformulate existing models into a single common framework we call Message Passing Neural Networks (MPNNs) and explore additional novel variations within this framework. Using MPNNs we demonstrate state of the art results on an important molecular property prediction benchmark; these results are strong enough that we believe future work should focus on datasets with larger molecules or more accurate ground truth labels.
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No need stupid moving texts.
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https://arxiv.org/abs/1603.05027
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Has the author looked at how GNNs are used in say predicting molecular properties[1] or molecule generation[2]? GNNs are the state of the art on those tasks and those problems are certainly much harder than the Cora/Citeseer/etc semisupervised node label prediction tasks.
[1] https://arxiv.org/abs/1704.01212
[2] http://proceedings.mlr.press/v80/jin18a/jin18a.pdf
My PyTorch hackathon submission. Basically I am showing that how PDEs on graphs can be solved with Message Passing class of torch_geometric and created a tool torch_pdegraph to facilitate this.
I work in the field of PDEs on graphs and I realized that Message passagin equation (the way of forward convolution) on graphs (https://arxiv.org/pdf/1704.01212.pdf) can also be used to solve some PDEs on graphs.
Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task.
Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small difference between training and test performance.
In honor of Alan Turing's hundredth birthday, I unwisely set out some thoughts about one of Turing's obsessions throughout his life, the question of physics and free will.