Neural Turing Machines

E899070

Neural Turing Machines are a class of neural network architectures that augment standard networks with differentiable external memory, enabling them to learn algorithmic and sequence-based tasks in a manner analogous to Turing machines.

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Statements (48)

Predicate Object
instanceOf differentiable computer
memory-augmented neural network
neural network architecture
comparedTo Gated Recurrent Unit NERFINISHED
Long Short-Term Memory NERFINISHED
controllerType recurrent neural network
describedInPaper Neural Turing Machines NERFINISHED
developedAt Google DeepMind NERFINISHED
field deep learning
machine learning
neural computation
hasAuthor Alex Graves NERFINISHED
Greg Wayne NERFINISHED
Ivo Danihelka NERFINISHED
hasComponent controller network
read head
write head
hasExternalMemory differentiable memory matrix
hasLimitation difficulty scaling to large problems
training instability
hasMemoryAccessMechanism soft attention over memory locations
hasProperty can generalize to longer sequences than seen during training
can learn algorithms from data
end-to-end differentiable
hasPublicationYear 2014
hasSuccessor Differentiable Neural Computer NERFINISHED
isInspiredBy Turing machine NERFINISHED
neural networks
memoryAddressingType content-based
hybrid addressing
location-based
optimizationObjective sequence prediction loss
task-specific loss
relatedConcept differentiable memory
memory-augmented neural networks
neural RAM NERFINISHED
supportsOperation content-based addressing
differentiable read
differentiable write
location-based addressing
trainedWith backpropagation through time
gradient descent
usedFor algorithmic tasks
associative recall task
copy task
repeat-copy task
sequence-based tasks
sorting task

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Neural Turing Machines (contributions) hasTitle Neural Turing Machines
subject surface form: Neural Turing Machines