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Showing posts from December, 2023

An Intro to Graph Convolutional Networks

Graph neural networks (GNNs) are deep learning networks that operate on graph data. These networks are increasingly getting popular as numerous real-world applications are easily modeled as graphs. Graphs are unlike images, text, time-series that are used in deep learning models. Graphs are of arbitrary size and complex topological structure. We represent graphs as a set of nodes and edges. In many instances, each node is associated with a feature vector. The adjacency matrix of a graph defines the presence of edges between the nodes. The ordering of nodes in a graph is arbitrary. These factors make it hard to use the existing deep learning architectures and call for an architecture suited to graphs as inputs. Permutation Invariance Architecture Since the nodes in a graph are arbitrarily ordered, it is possible that two adjacency matrices might be representing the same graph. So whatever architecture we plan for graph computation, it should be invariant to the ordering of nodes. This r

Whose Model is Better?

You and your friend are training a neural network for classification. Both of you are using identical training data. The data has four classes with 40% examples of cat images, 10% images of dogs, and 25% each of horse and sheep images. Since the deadline for the project is nearing, both of you decide to run only a few epochs and get to report writing. At the same time, the two of you have a friendly wager of $10 going to the winner of the better model. At the end of training, you find out that your model, Net1, is making 30% recognition errors and the resulting distribution of assigned labels to the training data is 25% each for four classes. As luck would have it, your friend's model, Net2, is also yielding 30% error rate but the assigned labels in the training set are different with 40% cats, 10% dogs, 10% horse, and 40% sheep. Since the error rate by both models is identical, your friend declares a tie. You on the other hand are insisting that your model Net1 is slightly better