LSTM - Long Short Term Memory
People
(Responsible)
External participants
Beringer Nicole
(Third-party beneficiary)
Eck Douglas
(Third-party beneficiary)
Graves Alex
(Third-party beneficiary)
Abstract
Most work in machine learning focuses on machines with reactive behavior. Recurrent neural networks or RNNs, however, are more general sequence processors inspired by human brains. They have adaptive feedback connections and are in principle as powerful as any computer. Until recently, however, RNNs could not learn to look far back into the past. But our novel RNN called "Long Short-Term Memory" (LSTM) overcomes the fundamental problems of traditional RNNs, and efficiently learns to solve many previously unlearnable tasks, including: Recognition of certain context sensitive languages; Reinforcement learning in partially observable environments; Metalearning of fast online learning algorithms; Music composition.