Vine copula (breast cancer)
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
- Massachusetts Institute of Technology (MIT),Rey Juan Carlos University
- Organisation type
- Academia,Academia
- Country
- United States of America, Spain
- Published
- 4 December 2018
- Authors
- Yi Sun, Alfredo Cuesta-Infante, Kalyan Veeramachaneni
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
- tokens
The dataset "describes 30 variables computed from a digitized image of a fine needle aspirate (FNA) of a breast mass and a binary variable indicating if the mass is benign or malignant. This dataset includes 569 instances.
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 8.5 × 10¹⁵ FLOP
- How it was established
- Hardware
"for a 6-dimensional data set of size 500, the RL algorithm finishes in approximately 15 minutes with a single GPU"" Assuming they used V100, fp16 and trained for similar amount of time: 31330000000000 FLOP / GPU / sec * 15 min * 60 sec / min * 0.3 [assumed utilization] = 8.4591e+15 FLOP
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Wall-clock time
- 0 hours
" for a 6-dimensional data set of size 500, the RL algorithm finishes in approximately 15 minutes with a single GPU"
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- Learning Vine Copula Models For Synthetic Data Generation
- Last updated
- 11 February 2026
What the numbers mean
What this model is
Vine copula (breast cancer) was published by Massachusetts Institute of Technology (MIT),Rey Juan Carlos University, in the country recorded as United States of America, during December 2018. The category the publisher falls under is academia,Academia.
It works in the domain of Vision, and is recorded as performing the task of image classification.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Producing it required arithmetic totalling around 8.5 × 10¹⁵ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
Vine copula (breast cancer) — common questions
Vine copula (breast cancer)— when was it released?
It was published in December 2018. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Vine copula (breast cancer)— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
Vine copula (breast cancer)— how much compute was used to train it?
Training consumed around 8.5 × 10¹⁵ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
Vine copula (breast cancer)— 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.
Vine copula (breast cancer)— 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.
Vine copula (breast cancer)— 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.
Vine copula (breast cancer)— who created it?
It was published by Massachusetts Institute of Technology (MIT),Rey Juan Carlos University, based in United States of America, an organisation categorised as 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.