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$\text{logsumexp}$ is an interesting little function that shows up surprisingly often in machine learning. Join me in this post to shed some light on $\text{logsumexp}$: where it lives, how it behaves, and how to interpret it.
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There’s a hot new dataset for spoken language understanding: SLURP. I’m excited about SLURP for a couple reasons:
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The Transducer (sometimes called the “RNN Transducer” or “RNN-T”, though it need not use RNNs) is a sequence-to-sequence model proposed by Alex Graves in “Sequence Transduction with Recurrent Neural Networks”. The paper was published at the ICML 2012 Workshop on Representation Learning. Graves showed that the Transducer was a sensible model to use for speech recognition, achieving good results on a small dataset (TIMIT).
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The name “predictive coding” has been applied to a number of engineering techniques and scientific theories. All these techniques and theories involve predicting future observations from past observations, but what exactly is meant by “coding” differs in each case. Here is a quick tour of some flavors of “predictive coding” and how they’re related.
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I’ve written a notebook introducing Hidden Markov Models (HMMs) with a PyTorch implementation of the forward algorithm, the Viterbi algorithm, and training a model on a text dataset—check it out here!
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Many interesting problems in artificial intelligence can be described in the following way:
Map a sequence of inputs $\mathbf{x}$ to the correct sequence of outputs $\mathbf{y}$.