Sparse Energy-Based Model
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
- New York University (NYU)
- Organisation type
- Academia
- Country
- United States of America
- Published
- 4 December 2006
- Authors
- M Ranzato, C Poultney, S Chopra, Y Cun
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Character recognition (OCR)
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
- 47,040,000 tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 1,601
Sources
Where this record came from and when it was last checked.
- Reference
- Efficient Learning of Sparse Representations with an Energy-Based Model
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Sparse Energy-Based Model was published by New York University (NYU), in the country recorded as United States of America, during December 2006. It comes out of an organisation categorised as academia.
It works in the domain of Vision, and is recorded as performing the task of character recognition (OCR).
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
It was trained on a corpus of about 47,040,000 tokens of text.
Answers
Sparse Energy-Based Model — common questions
Sparse Energy-Based Model— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Sparse Energy-Based Model— who created it?
It was published by New York University (NYU), based in United States of America, an organisation categorised as academia.
Sparse Energy-Based Model— when was it released?
It was published in December 2006. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Sparse Energy-Based Model— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of character recognition (OCR). A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
Sparse Energy-Based Model— what GPU do I need to run it?
None. This 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.
Sparse Energy-Based Model— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.