Layer-Norm Fast Weights RNN
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 Toronto,Google DeepMind,Google Brain
- Organisation type
- Academia,Industry,Industry
- Country
- Canada, United States of America
- Published
- 5 December 2016
- Authors
- Jimmy Ba, Geoffrey Hinton, Volodymyr Mnih, Joel Z. Leibo, Catalin Ionescu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Digit recognition, 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
- tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Using Fast Weights to Attend to the Recent Past
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Layer-Norm Fast Weights RNN was published by University of Toronto,Google DeepMind,Google Brain, in the country recorded as Canada, during December 2016. It comes out of an organisation categorised as academia,Industry,Industry.
It works in the domain of Vision, and is recorded as performing the task of digit recognition, Image classification.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
Layer-Norm Fast Weights RNN — common questions
Layer-Norm Fast Weights RNN— 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.
Layer-Norm Fast Weights RNN— who created it?
It was published by University of Toronto,Google DeepMind,Google Brain, based in Canada, an organisation categorised as academia,Industry,Industry.
Layer-Norm Fast Weights RNN— when was it released?
It was published in December 2016. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Layer-Norm Fast Weights RNN— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of digit recognition, Image classification. These are the areas it was designed around; they describe intent rather than a hard boundary.
Layer-Norm Fast Weights RNN— 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.
Layer-Norm Fast Weights RNN— 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.