Stacked Denoising Autoencoders
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
- University of Montreal / Université de Montréal,University of Toronto
- Organisation type
- Academia,Academia
- Country
- Canada
- Published
- 3 January 2010
- Authors
- P Vincent, H Larochelle, I Lajoie, Y Bengio
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Other
- Task
- Image classification
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
- 339,250,000 tokens
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
- Unknown
- Citations
- 7,411
Sources
Where this record came from and when it was last checked.
- Reference
- Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
- Last updated
- 1 January 2026
What the numbers mean
Where it came from
Stacked Denoising Autoencoders was published by University of Montreal / Université de Montréal,University of Toronto, in the country recorded as Canada, during January 2010. It comes out of an organisation categorised as academia,Academia.
It works in the domain of Other, and is recorded as performing the task of image classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Training consumed a corpus of around 339,250,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
Stacked Denoising Autoencoders — common questions
Stacked Denoising Autoencoders— what is it used for?
It works in the domain of Other, and is recorded as handling the task of image classification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Stacked Denoising Autoencoders— 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.
Stacked Denoising Autoencoders— 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.
Stacked Denoising Autoencoders— 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.
Stacked Denoising Autoencoders— who created it?
It was published by University of Montreal / Université de Montréal,University of Toronto, based in Canada, an organisation categorised as academia,Academia.
Stacked Denoising Autoencoders— when was it released?
It was published in January 2010. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.