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Visualizing Weights

The problem of understanding a neural network is a little bit like reverse engineering a large compiled binary of a computer program. In this analogy, the weights of the neural network are the compiled assembly instructions. At the end of the day, the weights are the fundamental thing you want to understand: how does this sequence of convolutions and matrix multiplications give rise to model behavior? Trying to understand artificial neural networks also has a lot in common with neuroscience, which tries to understand biological neural networks. As you may know, one major endeavor in modern neuroscience is mapping the connectomes of biological neural networks: which neurons connect to which. These connections, however, will only tell neuroscientists which weights are non-zero. Getting the weights – knowing whether a connection excites or inhibits, and by how much – would be a significant further step. One imagines neuroscientists might give a great deal to have the access to weights that those of us studying artificial neural networks get for free. And so, it’s rather surprising how little attention we actually give to looking at the weights of neural networks. There are a few exceptions to this, of course. It’s quite common for researchers to show pictures of the first layer weights in vision models (these are directly connected to RGB channels, so they’re easy to understand as images). In some work, especially historically, we see researchers reason about the weights of toy neural networks by hand. And we quite often see researchers discuss aggregate statistics of weights. But actually looking at the weights of a neural network other than the first layer is quite uncommon – to the best of our knowledge, mapping weights between hidden layers to meaningful algorithms is novel to the circuits project.

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