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Explain My Surprise: Learning Efficient Long-Term Memory by predicting uncertain outcomes

Artyom Sorokin · Nazar Buzun · Leonid Pugachev · Mikhail Burtsev

Hall J (level 1) #424

Keywords: [ POMDP ] [ RNN ] [ Reinforcement Learning ] [ Memory ] [ Information Theory ]


In many sequential tasks, a model needs to remember relevant events from the distant past to make correct predictions. Unfortunately, a straightforward application of gradient based training requires intermediate computations to be stored for every element of a sequence. This requires to store prohibitively large intermediate data if a sequence consists of thousands or even millions elements, and as a result, makes learning of very long-term dependencies infeasible. However, the majority of sequence elements can usually be predicted by taking into account only temporally local information. On the other hand, predictions affected by long-term dependencies are sparse and characterized by high uncertainty given only local information. We propose \texttt{MemUP}, a new training method that allows to learn long-term dependencies without backpropagating gradients through the whole sequence at a time. This method can potentially be applied to any recurrent architecture. LSTM network trained with \texttt{MemUP} performs better or comparable to baselines while requiring to store less intermediate data.

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