Bayesian automated hyperparameter tuning
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,University of Sherbrooke,Harvard University
- Organisation type
- Academia,Academia,Academia
- Country
- Canada, United States of America
- Published
- 2 December 2012
- Authors
- J Snoek, H Larochelle, RP Adams
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Other
- Task
- Mathematical simulation
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.
- Why it is tracked
- Highly cited
- Record confidence
- Unknown
- Citations
- 9,316
Sources
Where this record came from and when it was last checked.
- Reference
- Practical Bayesian optimization of machine learning algorithms
- Last updated
- 25 May 2026
What the numbers mean
What this model is
Bayesian automated hyperparameter tuning was published by University of Toronto,University of Sherbrooke,Harvard University, in Canada, in December 2012. academia,Academia,Academia is the category the publisher falls under.
It works in Other, and is recorded as doing mathematical simulation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
The training set ran to roughly 50,000 tokens.
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
Bayesian automated hyperparameter tuning — common questions
When was Bayesian automated hyperparameter tuning released?
Bayesian automated hyperparameter tuning was published in December 2012. 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 Bayesian automated hyperparameter tuning used for?
Bayesian automated hyperparameter tuning works in Other, and is recorded as handling mathematical simulation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Bayesian automated hyperparameter tuning?
None. Bayesian automated hyperparameter tuning 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 Bayesian automated hyperparameter tuning open source?
The licensing for Bayesian automated hyperparameter tuning 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 Bayesian automated hyperparameter tuning have?
No parameter count has been published for Bayesian automated hyperparameter tuning, which is why no memory or speed figure appears on this page.
Who created Bayesian automated hyperparameter tuning?
Bayesian automated hyperparameter tuning was published by University of Toronto,University of Sherbrooke,Harvard University, based in Canada, categorised as academia,Academia,Academia.
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.