Gated HORNN (3rd order)
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
- York University
- Organisation type
- Academia
- Country
- Canada
- Published
- 30 April 2016
- Authors
- Rohollah Soltani, Hui Jiang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
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.
- Parameters
- 9M
- Training data
- 22,400,000 tokens
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.
- Why it is tracked
- SOTA improvement
- Citations
- 77
- Benchmark data
- Gated HORNN (3rd order)
"Both FOFEbased pooling and gated HORNNs have achieved the stateof-the-art performance, i.e., 100 in perplexity on this task. To the best of our knowledge, this is the best reported performance on PTB under the same training condition."
Sources
Where this record came from and when it was last checked.
- Reference
- Higher Order Recurrent Neural Networks
- Last updated
- 11 February 2026
What the numbers mean
Background
Gated HORNN (3rd order) was published by York University, in Canada, in April 2016. It comes out of academia.
It works in Language, and is recorded as doing language modeling.
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 22,400,000 tokens went into training it.
The reason it appears in this catalogue at all is sOTA improvement.
Answers
Gated HORNN (3rd order) — common questions
Is Gated HORNN (3rd order) open source?
No. Gated HORNN (3rd order) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Gated HORNN (3rd order) have?
Gated HORNN (3rd order) has 9M parameters. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created Gated HORNN (3rd order)?
Gated HORNN (3rd order) was published by York University, based in Canada, categorised as academia.
When was Gated HORNN (3rd order) released?
Gated HORNN (3rd order) was published in April 2016. 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 Gated HORNN (3rd order) used for?
Gated HORNN (3rd order) works in Language, and is recorded as handling language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Gated HORNN (3rd order)?
None. Gated HORNN (3rd order) 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.
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.