triplefloat

3 ms·

1.
Building Models That Learn to Discover Structure and Relations(medium.com)
3 points by triplefloat 8y ago|0 comments
2.
by triplefloat 10y ago
Very exciting extension of Neural Turing Machines. As a side note: Gated Graph Sequence Neural Networks ( https://arxiv.org/abs/1511.05493 ) perform similarly or better on the bAbI tasks mentioned in the paper. The compa
3.
by triplefloat 10y ago
Mini-batching is indeed tricky, as you need the information of the complete local neighborhood for all nodes in a mini-batch. Let's say you select N nodes for a mini-batch, then you would also have to provide all nodes of the k-th orde
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by triplefloat 10y ago
Currently the framework only supports undirected graphs, so directed graphical models wouldn't be supported as input. I can't really judge how useful it would be to take a Bayesian net as input, sounds a bit hacky to me. But in pr
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by triplefloat 10y ago
In a graphical model, you'd explicitly model the probabilistic assumptions that you make with respect to the data. In this neural network-based approach the goal can be thought of more like learning a function that maps from some input
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by triplefloat 10y ago
Both graph-level and node-level classification are possible. Graph-level classification requires some from of pooling operation (simplest case: mean-pooling over all nodes, but there are more elaborate things one can do)
7.
Show HN: Graph Convolutional Networks – Intro to neural networks on graphs(tkipf.github.io)
106 points by triplefloat 10y ago|12 comments