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 the country recorded as Canada, during April 2016. It comes out of an organisation categorised as academia.
It works in the domain of Language, and is recorded as performing the task of 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
Training consumed a corpus of around 22,400,000 tokens of text.
The reason it appears in this catalogue at all: sOTA improvement.
Answers
Gated HORNN (3rd order) — common questions
Gated HORNN (3rd order)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Gated HORNN (3rd order)— how many parameters does it have?
It has a parameter count of 9M. 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.
Gated HORNN (3rd order)— who created it?
It was published by York University, based in Canada, an organisation categorised as academia.
Gated HORNN (3rd order)— when was it released?
It 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.
Gated HORNN (3rd order)— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Gated HORNN (3rd order)— 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.
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.