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In many sequential tasks, a model needs to remember relevant events from thedistant past to make correct predictions. Unfortunately, a straightforwardapplication of gradient based training requires intermediate computations to bestored for every element of a sequence. This requires to store prohibitivelylarge intermediate data if a sequence consists of thousands or even millionselements, and as a result, makes learning of very long-term dependenciesinfeasible. However, the majority of sequence elements can usually be predictedby taking into account only temporally local information. On the other hand,predictions affected by long-term dependencies are sparse and characterized byhigh uncertainty given only local information. We propose MemUP, a new trainingmethod that allows to learn long-term dependencies without backpropagatinggradients through the whole sequence at a time. This method can potentially beapplied to any recurrent architecture. LSTM network trained with MemUP performsbetter or comparable to baselines while requiring to store less intermediatedata.

Explain My Surprise: Learning Efficient Long-Term Memory by Predicting Uncertain Outcomes