Greedy layer-wise DNN training
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
- Organisation type
- Academia
- Country
- Canada
- Published
- 4 December 2006
- Authors
- Y Bengio, P Lamblin, D Popovici
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Other
- Task
- Image classification, Regression
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
- 107,100,000 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
- 5,605
Sources
Where this record came from and when it was last checked.
- Reference
- Greedy layer-wise training of deep networks
- Last updated
- 28 November 2025
What the numbers mean
About this model
Greedy layer-wise DNN training was published by University of Montreal / Université de Montréal, in Canada, in December 2006. The organisation is categorised as academia.
It works in Other, and is recorded as doing image classification, Regression.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
The training set ran to roughly 107,100,000 tokens.
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
Greedy layer-wise DNN training — common questions
Is Greedy layer-wise DNN training open source?
The licensing for Greedy layer-wise DNN training 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 Greedy layer-wise DNN training have?
No parameter count has been published for Greedy layer-wise DNN training, which is why no memory or speed figure appears on this page.
Who created Greedy layer-wise DNN training?
Greedy layer-wise DNN training was published by University of Montreal / Université de Montréal, based in Canada, categorised as academia.
When was Greedy layer-wise DNN training released?
Greedy layer-wise DNN training 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.
What is Greedy layer-wise DNN training used for?
Greedy layer-wise DNN training works in Other, and is recorded as handling image classification, Regression. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Greedy layer-wise DNN training?
None. Greedy layer-wise DNN training 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.
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.