Graph-based Semi-Supervised Learning (GSSL) Model on MNIST
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
- West Virginia University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 17 July 2019
- Authors
- Fariborz Taherkhani, Hadi Kazemi, Nasser M. Nasrabadi
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
- 60,000 tokens
batch size: 128 training steps and epochs unknown
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA GeForce GTX TITAN X
- Chips used
- 2
- Power draw
- 1.0 kW
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- Matrix Completion for Graph-Based Deep Semi-Supervised Learning
- Last updated
- 11 February 2026
What the numbers mean
What this model is
Graph-based Semi-Supervised Learning (GSSL) Model on MNIST was published by West Virginia University, in the country recorded as United States of America, during July 2019. The publishing organisation is categorised as 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.
What went into building it
It was trained on a corpus of about 60,000 tokens of text.
Answers
Graph-based Semi-Supervised Learning (GSSL) Model on MNIST — common questions
Graph-based Semi-Supervised Learning (GSSL) Model on MNIST— who created it?
It was published by West Virginia University, based in United States of America, an organisation categorised as academia.
Graph-based Semi-Supervised Learning (GSSL) Model on MNIST— when was it released?
It was published in July 2019. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Graph-based Semi-Supervised Learning (GSSL) Model on MNIST— 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.
Graph-based Semi-Supervised Learning (GSSL) Model on MNIST— 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.
Graph-based Semi-Supervised Learning (GSSL) Model on MNIST— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Graph-based Semi-Supervised Learning (GSSL) Model on MNIST— 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.