TCN (P-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
- Carnegie Mellon University (CMU),Intel Labs
- Organisation type
- Academia,Industry
- Country
- United States of America
- Published
- 15 February 2018
- Authors
- Shaojie Bai, J. Zico Kolter, Vladlen Koltun
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification, Digit recognition
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
- 42K
- Training data
- 60,000 tokens
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
- Record confidence
- Confident
- Citations
- 6,496
"For the permuted sequential MNIST, TCNs outperform state of the art results using recurrent nets (95.9%) with Zoneout+Recurrent BatchNorm (Cooijmans et al., 2016; Krueger et al., 2017), a highly optimized method for regularizing RNNs"
Sources
Where this record came from and when it was last checked.
- Reference
- Convolutional Sequence Modeling Revisited
- Last updated
- 25 May 2026
What the numbers mean
What this model is
TCN (P-MNIST) was published by Carnegie Mellon University (CMU),Intel Labs, in the country recorded as United States of America, during February 2018. It comes out of an organisation categorised as academia,Industry.
It works in the domain of Vision, and is recorded as performing the task of image classification, Digit recognition.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
It was trained on a corpus of about 60,000 tokens of text.
Its inclusion criterion: sOTA improvement.
Answers
TCN (P-MNIST) — common questions
TCN (P-MNIST)— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification, Digit recognition. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
TCN (P-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.
TCN (P-MNIST)— 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.
TCN (P-MNIST)— how many parameters does it have?
It has a parameter count of 42K. 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.
TCN (P-MNIST)— who created it?
It was published by Carnegie Mellon University (CMU),Intel Labs, based in United States of America, an organisation categorised as academia,Industry.
TCN (P-MNIST)— when was it released?
It was published in February 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.
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.