AI models
Translation-invariant MLP
Training compute
1.8×10¹⁰ FLOP
Parameters
816
Published
Jun 15, 1987
Translation-invariant MLP is an AI model developed by Carnegie Mellon University (CMU) (United States), first published in June 1987. It counts among the frontier models: the systems trained with the most compute of their moment.
Training it took an estimated 1.8×10¹⁰ FLOP of compute (estimation method: operation counting). The model has 816 parameters. It was trained on roughly 160 datapoints.
Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Carnegie Mellon University (CMU)
- Country of organization
- United States
- Task
- Object recognition
- Training compute
- 1.8×10¹⁰ FLOP
- Compute estimation method
- Operation counting
- Parameters
- 816
- Dataset size
- 160
- Epoch confidence
- Confident
More from Carnegie Mellon University (CMU)
SourceEpoch AI, 'AI Models'. Published online at epoch.ai. Retrieved 2026-07-29 from https://epoch.ai/data/ai-models. Licensed under CC BY 4.0.
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