LSTM can solve hard long time lag problems (1997) [16 citations — 7 self]
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@INPROCEEDINGS{Schmidhuber97lstmcan,author = {Jurgen Schmidhuber},
title = {LSTM can solve hard long time lag problems},
booktitle = {Advances in Neural Information Processing Systems 9},
year = {1997},
pages = {473--479},
publisher = {MIT Press}
}
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Abstract:
Standard recurrent nets cannot deal with long minimal time lags between relevant signals. Several recent NIPS papers propose alternative methods. We first show: problems used to promote various previous algorithms can be solved more quickly by random weight guessing than by the proposed algorithms. We then use LSTM, our own recent algorithm, to solve a hard problem that can neither be quickly solved by random search nor by any other recurrent net algorithm we are aware of. 1 TRIVIAL PREVIOUS LONG TIME LAG PROBLEMS Traditional recurrent nets fail in case of long minimal time lags between input signals and corresponding error signals [7, 3]. Many recent papers propose alternative methods, e.g., [16, 12, 1, 5, 9]. For instance, Bengio et al. investigate methods such as simulated annealing, multi-grid random search, time-weighted pseudo-Newton optimization, and discrete error propagation [3]. They also propose an EM approach
