Sequence transduction with recurrent neural networks
E736825
"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.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Sequence transduction with recurrent neural networks canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T8482850 — 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: Sequence transduction with recurrent neural networks Context triple: [Alex Graves, notableWork, Sequence transduction with recurrent neural networks]
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A.
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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B.
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
"Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation" is a seminal research paper that introduced the RNN encoder–decoder architecture to learn continuous phrase representations for improving statistical machine translation quality.
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C.
Distributed Representations of Sentences and Documents
"Distributed Representations of Sentences and Documents" is a seminal machine learning paper that introduced the Paragraph Vector (Doc2Vec) method for learning continuous vector representations of variable-length text such as sentences, paragraphs, and documents.
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D.
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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E.
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
"Long-term Recurrent Convolutional Networks for Visual Recognition and Description" is a research paper that introduces a deep learning architecture combining convolutional and recurrent neural networks to perform tasks like video recognition and automatic image or video captioning.
- 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: Sequence transduction with recurrent neural networks Target entity description: "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.
-
A.
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.
-
B.
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
"Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation" is a seminal research paper that introduced the RNN encoder–decoder architecture to learn continuous phrase representations for improving statistical machine translation quality.
-
C.
Distributed Representations of Sentences and Documents
"Distributed Representations of Sentences and Documents" is a seminal machine learning paper that introduced the Paragraph Vector (Doc2Vec) method for learning continuous vector representations of variable-length text such as sentences, paragraphs, and documents.
-
D.
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.
-
E.
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
"Long-term Recurrent Convolutional Networks for Visual Recognition and Description" is a research paper that introduces a deep learning architecture combining convolutional and recurrent neural networks to perform tasks like video recognition and automatic image or video captioning.
- F. None of above. chosen
Statements (45)
| Predicate | Object |
|---|---|
| instanceOf | research paper ⓘ |
| appliedTo |
sequence labeling
ⓘ
speech recognition ⓘ time series modeling ⓘ |
| author | Alex Graves NERFINISHED ⓘ |
| contribution |
demonstrated end-to-end sequence mapping with RNNs
ⓘ
influenced attention-based neural network architectures ⓘ influenced modern sequence-to-sequence models ⓘ introduced an RNN-based framework for mapping input sequences to output sequences ⓘ proposed a sequence transduction model using recurrent neural networks ⓘ |
| coreConcept |
end-to-end trainable sequence transduction
ⓘ
joint modeling of alignment and labeling ⓘ probabilistic modeling of output sequences conditioned on input sequences ⓘ |
| field |
artificial intelligence
ⓘ
deep learning ⓘ machine learning ⓘ |
| focusesOn |
alignment-free sequence modeling
ⓘ
end-to-end learning of sequence mappings ⓘ mapping variable-length input sequences to variable-length output sequences ⓘ |
| goal | to learn a direct mapping from input sequences to output sequences without pre-specified alignments ⓘ |
| hasAuthor | Alex Graves NERFINISHED ⓘ |
| impact |
considered a seminal work in sequence modeling with RNNs
ⓘ
helped establish RNNs as a general framework for sequence transduction tasks ⓘ |
| influenced |
RNN-based encoder-decoder architectures
ⓘ
attention mechanisms in sequence models ⓘ end-to-end speech recognition systems ⓘ neural machine translation models ⓘ sequence-to-sequence with neural networks ⓘ |
| influencedBy |
connectionist temporal classification
NERFINISHED
ⓘ
recurrent neural network language models ⓘ |
| language | English ⓘ |
| proposedBy | Alex Graves NERFINISHED ⓘ |
| relatedTo |
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
NERFINISHED
ⓘ
attention-based encoder-decoder models ⓘ sequence-to-sequence learning ⓘ |
| researchArea |
recurrent neural networks
ⓘ
sequence modeling ⓘ sequence transduction ⓘ sequence-to-sequence learning ⓘ speech recognition ⓘ |
| typeOfModel | RNN-based sequence transducer ⓘ |
| usesMethod |
bidirectional recurrent neural networks
ⓘ
connectionist temporal classification NERFINISHED ⓘ probabilistic sequence modeling ⓘ recurrent neural networks ⓘ |
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: Sequence transduction with recurrent neural networks Description of subject: "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.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.