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Goals of Interpretability
In my last post, I looked at the gap that arises when we delegate parts of our thought processes to algorithmic models, rather than incorporating the rules they identify directly into our mental models, like we do with traditional statistics. I … Continue reading
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Rolling and Unrolling RNNs
A while back, I discussed Recurrent Neural Networks (RNNs), a type of artificial neural network in which some of the connections between neurons point “backwards”. When a sequence of inputs is fed into such a network, the backward arrows feed … Continue reading
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Duality and Coclustering
In mathematics, the term “duality” usually refers to a relationship between two object, or types of objects, that is somehow symmetric. (Note that this is different from some other uses of the term outside of mathematics.) For example, if we … Continue reading
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TILO/PRC Clustering
In the last few weeks, we’ve looked at a number of different algorithms for dividing a data set into clusters, and a few ways of measuring how good the clusters found by such an algorithm are. This week, I’m going … Continue reading
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Graphs and networks
In last week’s post, I discussed the difference between the extrinsic and intrinsic structures of a data set. The extrinsic structure, which has to do with how the data points sit in the data space, is encoded by the vector … Continue reading
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The Shape of Data 