Generating sequences with recurrent neural networks
E736826
"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.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Generating sequences with recurrent neural networks canonical | 1 |
Statements (45)
| Predicate | Object |
|---|---|
| instanceOf | research paper ⓘ |
| appliedTo |
handwriting synthesis
ⓘ
sequence prediction tasks ⓘ text generation ⓘ |
| author | Alex Graves NERFINISHED ⓘ |
| contribution |
advanced the use of RNNs for handwriting synthesis
ⓘ
demonstrated sampling-based sequence generation from trained RNNs ⓘ introduced techniques for stable training of RNNs for sequence generation ⓘ popularized character-level recurrent neural networks for text ⓘ showed that RNNs can generate coherent sequences over long time spans ⓘ |
| demonstrates |
character-level text generation
ⓘ
end-to-end sequence generation ⓘ powerful sequence modeling capabilities of RNNs ⓘ style-conditioned handwriting generation ⓘ text-conditioned handwriting generation ⓘ unconstrained handwriting synthesis ⓘ |
| field |
artificial intelligence
ⓘ
deep learning ⓘ machine learning ⓘ sequence modeling ⓘ |
| focusesOn |
character-level language modeling
ⓘ
handwriting generation ⓘ online sequence prediction ⓘ sequence generation ⓘ text generation ⓘ |
| hasImpact |
highly influential in deep learning for sequences
ⓘ
widely cited in the RNN literature ⓘ |
| influenced |
character-level language models
ⓘ
generative models for handwriting ⓘ neural text generation research ⓘ sequence-to-sequence modeling ⓘ |
| influencedBy |
earlier work on recurrent neural networks
ⓘ
long short-term memory architecture ⓘ |
| shows |
RNNs can generate readable character-level text
ⓘ
RNNs can generate realistic handwriting trajectories ⓘ RNNs can model complex temporal dependencies ⓘ |
| topic |
conditional sequence generation
ⓘ
modeling long-range dependencies in sequences ⓘ probabilistic sequence modeling ⓘ sampling from neural sequence models ⓘ training recurrent networks with gradient descent ⓘ |
| usesDataset |
handwriting datasets
ⓘ
text corpora ⓘ |
| usesMethod |
long short-term memory
ⓘ
recurrent neural networks ⓘ |
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.