LSTM networks | DisamKB

All labels observed (3)

Label	Occurrences
Long Short-Term Memory network	2
LSTM	1
LSTM networks canonical	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 NERFINISHED ⓘ
addressesProblem	exploding gradient problem ⓘ vanishing gradient problem ⓘ
comparedTo	simple recurrent neural network ⓘ
designedBy	Jürgen Schmidhuber NERFINISHED ⓘ Sepp Hochreiter NERFINISHED ⓘ
fullName	Long Short-Term Memory network NERFINISHED ⓘ
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 NERFINISHED ⓘ stacked LSTM NERFINISHED ⓘ
implementedIn	Keras NERFINISHED ⓘ MXNet NERFINISHED ⓘ PyTorch NERFINISHED ⓘ TensorFlow NERFINISHED ⓘ Theano NERFINISHED ⓘ
improvesOver	simple recurrent neural network ⓘ
publicationYear	1997 ⓘ
publishedIn	Neural Computation NERFINISHED ⓘ
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

Referenced by (4)

Full triples — surface form annotated when it differs from this entity's canonical label.

MXNet → supportsModelType → LSTM networks ⓘ

Sequence to Sequence Learning with Neural Networks → usesModel → LSTM networks ⓘ

this entity surface form: Long Short-Term Memory network

Long-term Recurrent Convolutional Networks for Visual Recognition and Description → relatedTo → LSTM networks ⓘ

this entity surface form: LSTM

Show and Tell: A Neural Image Caption Generator → uses → LSTM networks ⓘ