Translation-invariant MLP
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)
- Organisation type
- Academia
- Country
- United States of America
- Published
- 15 June 1987
- Authors
- Geoffrey E. Hinton
What it does
The problem areas the model was built for. A model can carry several of each.
- Task
- Object 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
- 0.8K
- Training data
- 160 tokens
- Epochs
- 23,020
Network: 12-60-6-16 Weights: 6*60+60*6+6*16=816 Layer 2 was only sparsely connected to input layer
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 1.8 × 10¹⁰ FLOP
- How it was established
- Operation counting
FLOPs: 2*816*3*160*23020=18032947200=1.8e10
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Frontier model
- Yes
- Why it is tracked
- Historical significance
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Learning Translation Invariant Recognition in a Massively Parallel Network
- Last updated
- 28 November 2025
What the numbers mean
Background
Translation-invariant MLP was published by Carnegie Mellon University (CMU), in the country recorded as United States of America, during June 1987. The category the publisher falls under is academia.
and is recorded as performing the task of object 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
Producing it required arithmetic totalling around 1.8 × 10¹⁰ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 160 tokens of text.
The reason it appears in this catalogue at all: historical significance.
Answers
Translation-invariant MLP — common questions
Translation-invariant MLP— how many parameters does it have?
It has a parameter count of 0.8K. Network: 12-60-6-16 Weights: 6*60+60*6+6*16=816 Layer 2 was only sparsely connected to input layer. 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.
Translation-invariant MLP— who created it?
It was published by Carnegie Mellon University (CMU), based in United States of America, an organisation categorised as academia.
Translation-invariant MLP— when was it released?
It was published in June 1987. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Translation-invariant MLP— what is it used for?
and is recorded as handling the task of object recognition. 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.
Translation-invariant MLP— how much compute was used to train it?
Training consumed around 1.8 × 10¹⁰ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
Translation-invariant MLP— 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.
Translation-invariant MLP— 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.
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.