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.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Neural Turing Machines canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T11003642 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Neural Turing Machines Context triple: [Neural Turing Machines, hasTitle, Neural Turing Machines]
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A.
Neural Turing Machines (contributions)
Neural Turing Machines (contributions) refers to Oriol Vinyals’s work on augmenting neural networks with differentiable external memory to enable algorithmic reasoning and sequence learning beyond traditional architectures.
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B.
Differentiable Neural Computers
Differentiable Neural Computers are a type of neural network architecture that augments traditional networks with an external, differentiable memory module, enabling them to learn algorithmic and reasoning tasks end-to-end.
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C.
Sequence to Sequence Learning with Neural Networks
"Sequence to Sequence Learning with Neural Networks" is a seminal 2014 paper that introduced the sequence-to-sequence (seq2seq) neural network framework for tasks like machine translation, laying the groundwork for many modern NLP models.
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D.
Sequence transduction with recurrent neural networks
"Sequence transduction with recurrent neural networks" is a seminal research paper by Alex Graves that introduced powerful RNN-based methods for mapping input sequences to output sequences, influencing modern sequence-to-sequence and attention models in machine learning.
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E.
LSTM networks
LSTM networks are a type of recurrent neural network architecture designed to effectively capture long-term dependencies in sequential data by using gated memory cells.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Neural Turing Machines Target entity description: 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.
-
A.
Neural Turing Machines (contributions)
Neural Turing Machines (contributions) refers to Oriol Vinyals’s work on augmenting neural networks with differentiable external memory to enable algorithmic reasoning and sequence learning beyond traditional architectures.
-
B.
Differentiable Neural Computers
Differentiable Neural Computers are a type of neural network architecture that augments traditional networks with an external, differentiable memory module, enabling them to learn algorithmic and reasoning tasks end-to-end.
-
C.
Sequence to Sequence Learning with Neural Networks
"Sequence to Sequence Learning with Neural Networks" is a seminal 2014 paper that introduced the sequence-to-sequence (seq2seq) neural network framework for tasks like machine translation, laying the groundwork for many modern NLP models.
-
D.
Sequence transduction with recurrent neural networks
"Sequence transduction with recurrent neural networks" is a seminal research paper by Alex Graves that introduced powerful RNN-based methods for mapping input sequences to output sequences, influencing modern sequence-to-sequence and attention models in machine learning.
-
E.
LSTM networks
LSTM networks are a type of recurrent neural network architecture designed to effectively capture long-term dependencies in sequential data by using gated memory cells.
- F. None of above. chosen
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 ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Neural Turing Machines Description of subject: 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.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.
subject surface form:
Neural Turing Machines