LSTMs are explicitly designed to avoid the long-term dependency problem. 1 They work tremendously well on a large variety of problems, and are now widely used. They were introduced by Hochreiter & Schmidhuber (1997), and were refined and popularized by many people in following work. Long Short Term Memory networks – usually just called “LSTMs” – are a special kind of RNN, capable of learning long-term dependencies. Thankfully, LSTMs don’t have this problem! LSTM Networks (1994), who found some pretty fundamental reasons why it might be difficult. The problem was explored in depth by Hochreiter (1991) and Bengio, et al. Sadly, in practice, RNNs don’t seem to be able to learn them. In theory, RNNs are absolutely capable of handling such “long-term dependencies.” A human could carefully pick parameters for them to solve toy problems of this form. In such cases, where the gap between the relevant information and the place that it’s needed is small, RNNs can learn to use the past information. If we are trying to predict the last word in “the clouds are in the sky,” we don’t need any further context – it’s pretty obvious the next word is going to be sky. For example, consider a language model trying to predict the next word based on the previous ones. Sometimes, we only need to look at recent information to perform the present task. If RNNs could do this, they’d be extremely useful. One of the appeals of RNNs is the idea that they might be able to connect previous information to the present task, such as using previous video frames might inform the understanding of the present frame. It’s these LSTMs that this essay will explore.
Almost all exciting results based on recurrent neural networks are achieved with them. But they really are pretty amazing.Įssential to these successes is the use of “LSTMs,” a very special kind of recurrent neural network which works, for many tasks, much much better than the standard version. I’ll leave discussion of the amazing feats one can achieve with RNNs to Andrej Karpathy’s excellent blog post, The Unreasonable Effectiveness of Recurrent Neural Networks. They’re the natural architecture of neural network to use for such data.Īnd they certainly are used! In the last few years, there have been incredible success applying RNNs to a variety of problems: speech recognition, language modeling, translation, image captioning… The list goes on. This chain-like nature reveals that recurrent neural networks are intimately related to sequences and lists.
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