LSTM networks
E814035
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.
All labels observed (7)
| Label | Occurrences |
|---|---|
| LSTM | 8 |
| Long Short-Term Memory | 7 |
| LSTM networks canonical | 4 |
| Long Short-Term Memory network | 3 |
| LSTM network | 1 |
| peephole LSTM | 1 |
| stacked LSTM | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T9674953 — 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: LSTM networks Context triple: [MXNet, supportsModelType, LSTM networks]
-
A.
Generating sequences with recurrent neural networks
"Generating Sequences with Recurrent Neural Networks" is a highly influential research paper by Alex Graves that advanced the use of RNNs for tasks like handwriting and text generation by demonstrating powerful sequence modeling and generation capabilities.
-
B.
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.
-
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.
Connectionist Temporal Classification
Connectionist Temporal Classification is a neural network training algorithm designed for sequence labeling tasks where input and output lengths differ and alignments are unknown, widely used in speech and handwriting recognition.
-
E.
Supervised Sequence Labelling with Recurrent Neural Networks
Supervised Sequence Labelling with Recurrent Neural Networks is a foundational monograph that systematically presents the theory, architectures, and training methods for applying recurrent neural networks to tasks such as speech recognition, handwriting recognition, and other sequence labeling problems.
- 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: LSTM networks Target entity description: 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.
-
A.
Generating sequences with recurrent neural networks
"Generating Sequences with Recurrent Neural Networks" is a highly influential research paper by Alex Graves that advanced the use of RNNs for tasks like handwriting and text generation by demonstrating powerful sequence modeling and generation capabilities.
-
B.
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.
-
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.
Connectionist Temporal Classification
Connectionist Temporal Classification is a neural network training algorithm designed for sequence labeling tasks where input and output lengths differ and alignments are unknown, widely used in speech and handwriting recognition.
-
E.
Supervised Sequence Labelling with Recurrent Neural Networks
Supervised Sequence Labelling with Recurrent Neural Networks is a foundational monograph that systematically presents the theory, architectures, and training methods for applying recurrent neural networks to tasks such as speech recognition, handwriting recognition, and other sequence labeling problems.
- F. None of above. chosen
Statements (52)
| Predicate | Object |
|---|---|
| instanceOf |
neural network model
ⓘ
recurrent neural network architecture ⓘ sequence modeling method ⓘ |
| abbreviation |
LSTM
ⓘ
linked to:
LSTM networks
|
| addressesProblem |
exploding gradient problem
ⓘ
vanishing gradient problem ⓘ |
| comparedTo | simple recurrent neural network ⓘ |
| designedBy |
Jürgen Schmidhuber
ⓘ
Sepp Hochreiter ⓘ |
| fullName |
Long Short-Term Memory network
ⓘ
linked to:
LSTM networks
|
| hasComponent |
cell state
ⓘ
forget gate ⓘ hidden state ⓘ input gate ⓘ memory cell ⓘ output gate ⓘ recurrent connections ⓘ |
| hasProperty |
capable of modeling long-term dependencies
ⓘ
gated architecture ⓘ mitigates vanishing gradient problem ⓘ supports many-to-many mapping ⓘ supports many-to-one mapping ⓘ supports one-to-many mapping ⓘ supports online learning ⓘ supports sequence-to-sequence learning ⓘ supports variable-length sequences ⓘ trainable with backpropagation through time ⓘ |
| hasVariant |
attention-based LSTM
ⓘ
bidirectional LSTM ⓘ convolutional LSTM ⓘ coupled input-forget gate LSTM ⓘ peephole LSTM ⓘ
linked to:
LSTM networks
stacked LSTM ⓘ
linked to:
LSTM networks
|
| implementedIn |
Keras
ⓘ
MXNet ⓘ PyTorch ⓘ TensorFlow ⓘ Theano ⓘ |
| improvesOver | simple recurrent neural network ⓘ |
| publicationYear | 1997 ⓘ |
| publishedIn | Neural Computation ⓘ |
| usedFor |
anomaly detection in sequences
ⓘ
handwriting recognition ⓘ language modeling ⓘ machine translation ⓘ music generation ⓘ natural language processing ⓘ sequence modeling ⓘ speech recognition ⓘ text generation ⓘ time series forecasting ⓘ video captioning ⓘ |
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: LSTM networks Description of subject: 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.
Referenced by (25)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: LSTM networks
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
→
relatedTo
→
LSTM
ⓘ
linked to: LSTM networks
linked to: LSTM networks
linked to: LSTM networks
linked to: LSTM networks
linked to: LSTM networks
linked to: LSTM networks
linked to: LSTM networks
linked to: LSTM networks
subject linked to:
LSTM networks
linked to: LSTM networks
subject linked to:
LSTM networks
linked to: LSTM networks
subject linked to:
LSTM networks
linked to: LSTM networks
subject linked to:
GU
linked to: LSTM networks
linked to: LSTM networks
linked to: LSTM networks
linked to: LSTM networks
linked to: LSTM networks
linked to: LSTM networks