Max-Margin Markov 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
- Stanford University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 1 March 2004
- Authors
- B. Taskar, C. Guestrin, and D. Koller
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision, Language
- Task
- Image classification, Text 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
- 4,800 tokens
The data set is divided into 10 folds of ∼ 600 training and ∼ 5500 testing examples. The accuracy results, ... are averages over the 10 folds
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 1,764
Sources
Where this record came from and when it was last checked.
- Reference
- Max-margin markov networks
- Last updated
- 28 November 2025
What the numbers mean
About this model
Max-Margin Markov Networks was published by Stanford University, in United States of America, in March 2004. The organisation is categorised as academia.
It works in Vision, Language, and is recorded as doing image classification, Text classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Around 4,800 tokens went into training it.
Answers
Max-Margin Markov Networks — common questions
What is Max-Margin Markov Networks used for?
Max-Margin Markov Networks works in Vision, Language, and is recorded as handling image classification, Text classification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Max-Margin Markov Networks?
None. Max-Margin Markov Networks 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 Max-Margin Markov Networks open source?
The licensing for Max-Margin Markov Networks 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 Max-Margin Markov Networks have?
No parameter count has been published for Max-Margin Markov Networks, which is why no memory or speed figure appears on this page.
Who created Max-Margin Markov Networks?
Max-Margin Markov Networks was published by Stanford University, based in United States of America, categorised as academia.
When was Max-Margin Markov Networks released?
Max-Margin Markov Networks was published in March 2004. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.