AI models
Deep Belief Nets
Parameters
1.6M
Published
Jul 18, 2006
Deep Belief Nets is an AI model developed by University of Toronto and National University of Singapore (Canada and Singapore), first published in July 2006. It works in the vision domain, on tasks such as character recognition (ocr).
Epoch AI has no training-compute estimate for this model. The model has 1,600,000 parameters. It was trained on roughly 47.1M datapoints.
The reference paper has 16,071 citations.
Full record
- Organization
- University of Toronto, National University of Singapore
- Country of organization
- Canada, Singapore
- Domain
- Vision
- Task
- Character recognition (OCR)
- Parameters
- 1,600,000
- Dataset size
- 47.1M
- Training time
- 168 h
- Citations
- 16,071
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.
← All ai models