Empirical evaluation of deep architectures
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
- 1 June 2007
- Authors
- Hugo Larechelle, Dumithru Erhan, Aaron C Courville, James Bergsta, Yoshua 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
- 50,000 tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
- Citations
- 1,185
Sources
Where this record came from and when it was last checked.
- Reference
- An empirical evaluation of deep architectures on problems with many factors of variation
- Last updated
- 1 January 2026
What the numbers mean
About this model
Empirical evaluation of deep architectures was published by University of Montreal / Université de Montréal, in Canada, in June 2007. academia is the category the publisher falls under.
It works in Other, and is recorded as doing image classification.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Around 50,000 tokens went into training it.
Answers
Empirical evaluation of deep architectures — common questions
Who created Empirical evaluation of deep architectures?
Empirical evaluation of deep architectures was published by University of Montreal / Université de Montréal, based in Canada, categorised as academia.
When was Empirical evaluation of deep architectures released?
Empirical evaluation of deep architectures was published in June 2007. 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 Empirical evaluation of deep architectures used for?
Empirical evaluation of deep architectures works in Other, and is recorded as handling image classification. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run Empirical evaluation of deep architectures?
None. Empirical evaluation of deep architectures 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 Empirical evaluation of deep architectures open source?
The licensing for Empirical evaluation of deep architectures 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 Empirical evaluation of deep architectures have?
No parameter count has been published for Empirical evaluation of deep architectures, which is why no memory or speed figure appears on this page.
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.