Deep rectifier networks
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
- 13 April 2011
- Authors
- Xavier Glorot, Antoine Bordes, Yoshua Bengio
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- 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
- 81,920,000 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
- 8,616
Sources
Where this record came from and when it was last checked.
- Reference
- Deep sparse rectifier neural networks
- Last updated
- 1 January 2026
What the numbers mean
Background
Deep rectifier networks was published by University of Montreal / Université de Montréal, in the country recorded as Canada, during April 2011. The category the publisher falls under is academia.
It works in the domain of Vision, and is recorded as performing the task of image classification.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
The training set ran to roughly 81,920,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
Deep rectifier networks — common questions
Deep rectifier networks— who created it?
It was published by University of Montreal / Université de Montréal, based in Canada, an organisation categorised as academia.
Deep rectifier networks— when was it released?
It was published in April 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.
Deep rectifier networks— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. These are the areas it was designed around; they describe intent rather than a hard boundary.
Deep rectifier networks— 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.
Deep rectifier networks— 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.
Deep rectifier networks— 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.
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.