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R2RT Toggle navigation R2RT Sun 08 April 2018 Synthetic Gradients with Tensorflow I stumbled upon Max Jaderberg's Synthetic Gradients paper while thinking about different forms of communication between neural modules. It's a simple idea: rather than compute gradients through backpropagation, we can train a model to predict what those gradients will be, and use our prediction to update our weights. I wanted to try using this in my own work and didn't find a Tensorflow implementation to my liking, so here is mine. I also take this opportunity to (attempt to) answer one of the questions I had while reading the paper: why not use synthetic loss instead of synthetic gradients? Wed 15 February 2017 Deconstruction with Discrete Embeddings In my post Beyond Binary, I showed how easy it is to create trainable "one-hot" neurons with the straight-through estimator. My motivation for this is made clear in this post, in which I demonstrate the potential o...
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