On vacation riding around the Maryland Eastern Shore, but I’m also trying to see if I can connect the ML concept of attention to population scale thinking
Which suggested this paper: Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction
- Current state-of-the-art machine translation systems are based on encoder-decoder architectures, that first encode the input sequence, and then generate an output sequence based on the input encoding. Both are interfaced with an attention mechanism that recombines a fixed encoding of the source tokens based on the decoder state. We propose an alternative approach which instead relies on a single 2D convolutional neural network across both sequences. Each layer of our network re-codes source tokens on the basis of the output sequence produced so far. Attention-like properties are therefore pervasive throughout the network. Our model yields excellent results, outperforming state-of-the-art encoder-decoder systems, while being conceptually simpler and having fewer parameters.
And here’s a Tensorflow tutorial
Which led to this nice tutorial: Neural Machine Translation (seq2seq) Tutorial
And here are a nice set of posts with better visualizations: