Visualizing memorization in RNNs
Inspecting gradient magnitudes in context can be a powerful tool to see when recurrent units use short-term or long-term contextual understanding. This connectivity visualization shows how strongly previous input characters influence the current target character in an autocomplete problem. For example, in the prediction of “grammar” the GRU RNN initially uses long-term memorization but as more cha
The Unreasonable Effectiveness of Recurrent Neural Networks
May 21, 2015 There’s something magical about Recurrent Neural Networks (RNNs). I still remember when I trained my first recurrent network for Image Captioning. Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of images that were on the edge of making sense. Sometimes the ratio of how simpl
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