Recursive Neural Network
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Stanford University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 28 June 2011
- Authors
- R. Socher, Cliff Chiung-Yu Lin, A. Ng, Christopher D. Manning
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision, Language
- Task
- Representation learning
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- 573,285 tokens
Full WSJ dataset: 1000000 words (https://catalog.ldc.upenn.edu/LDC99T42) Using 20 out of 24 splits for training: 1000000*(20/24)=833333.333
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Highly cited
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Parsing natural scenes and natural language with recursive neural networks
- Last updated
- 28 November 2025
What the numbers mean
About this model
Recursive Neural Network was published by Stanford University, in United States of America, in June 2011. It comes out of academia.
It works in Vision, Language, and is recorded as doing representation learning.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
The training set ran to roughly 573,285 tokens.
The reason it appears in this catalogue at all is highly cited.
Answers
Recursive Neural Network — common questions
What GPU do I need to run Recursive Neural Network?
None. Recursive Neural Network is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is Recursive Neural Network open source?
The licensing for Recursive Neural Network was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does Recursive Neural Network have?
No parameter count has been published for Recursive Neural Network, which is why no memory or speed figure appears on this page.
Who created Recursive Neural Network?
Recursive Neural Network was published by Stanford University, based in United States of America, categorised as academia.
When was Recursive Neural Network released?
Recursive Neural Network was published in June 2011. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is Recursive Neural Network used for?
Recursive Neural Network works in Vision, Language, and is recorded as handling representation learning. These are the areas it was designed around; they describe intent rather than a hard boundary.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.