Semi-Supervised Embedding for DL
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
- Google,NUANCE Communications,IDIAP,University of Illinois Urbana-Champaign (UIUC)
- Organisation type
- Industry,Industry,Academia,Academia
- Country
- United States of America, Switzerland
- Published
- 5 July 2008
- Authors
- Jason Weston, Frederick, Ratle, Hossein Mobahi, Ronan Collobert
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Other
- Task
- Image classification, Language Structure Modeling, 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
- 632,000,000 tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
- Citations
- 1,087
Sources
Where this record came from and when it was last checked.
- Reference
- Deep Learning via Semi-Supervised Embedding
- Last updated
- 28 November 2025
What the numbers mean
About this model
Semi-Supervised Embedding for DL was published by Google,NUANCE Communications,IDIAP,University of Illinois Urbana-Champaign (UIUC), in United States of America, in July 2008. industry,Industry,Academia,Academia is the category the publisher falls under.
It works in Other, and is recorded as doing image classification, Language Structure Modeling, Text classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Around 632,000,000 tokens went into training it.
Answers
Semi-Supervised Embedding for DL — common questions
Who created Semi-Supervised Embedding for DL?
Semi-Supervised Embedding for DL was published by Google,NUANCE Communications,IDIAP,University of Illinois Urbana-Champaign (UIUC), based in United States of America, categorised as industry,Industry,Academia,Academia.
When was Semi-Supervised Embedding for DL released?
Semi-Supervised Embedding for DL was published in July 2008. 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 Semi-Supervised Embedding for DL used for?
Semi-Supervised Embedding for DL works in Other, and is recorded as handling image classification, Language Structure Modeling, Text classification. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run Semi-Supervised Embedding for DL?
None. Semi-Supervised Embedding for DL 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 Semi-Supervised Embedding for DL open source?
The licensing for Semi-Supervised Embedding for DL 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 Semi-Supervised Embedding for DL have?
No parameter count has been published for Semi-Supervised Embedding for DL, 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.