R2rt.com Website Review

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? 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 of discrete embeddings. In short, discrete embeddings allow for explicit deconstruction of inherently fuzzy data, which allows us to apply explicit reasoning and algorithms over the data, and communicate fuzzy ideas with concrete symbols. Using discrete embeddings, we can (1) create a language model over the embeddings, which immediately gives us access to RNN-based generation of internal embeddings (and sequences thereof), and (2) index sub-parts of the embeddings, instead of entire embedding vectors, which gives us (i.e., our agents) access to search techniques that go beyond cosine similarity, such as phrase search and search using lightweight structure. decision. For example, we might want our neural network to make a choice between several categories (in the form of a one-hot vector) or we might want it to make a choice between ordered categories (e.g., a scale of 1 to 10). It's rather easy to extend the straight-through estimator to work well on both of these cases, and I thought I would share my work in this post. I share code for implementing ternary and one-hot neurons in Tensorflow, and show that they can learn to solve MNIST. The default approach to initializing the state of an RNN is to use a zero state. This often works well, particularly for