AI models
Dropout-LSTM+Noise(Bernoulli) (WT2)
Training compute
1.3×10¹⁷ FLOP
Parameters
51M
Published
May 3, 2018
Dropout-LSTM+Noise(Bernoulli) (WT2) is an AI model developed by Columbia University, New York University (NYU) and Princeton University (United States), first published in May 2018. It works in the language domain, on tasks such as language modeling.
Training it took an estimated 1.3×10¹⁷ FLOP of compute (estimation method: operation counting). The model has 51,000,000 parameters. It was trained on roughly 2M datapoints.
Access: Unreleased. Its weights are not openly released. The reference paper has 27 citations. Epoch AI rates the confidence of this record as likely.
Full record
- Organization
- Columbia University, New York University (NYU), Princeton University
- Country of organization
- United States
- Domain
- Language
- Task
- Language modeling
- Training compute
- 1.3×10¹⁷ FLOP
- Compute estimation method
- Operation counting
- Parameters
- 51,000,000
- Dataset size
- 2M
- Numerical format
- FP32
- Model accessibility
- Unreleased
- Open weights
- No
- Citations
- 27
- Epoch confidence
- Likely
More from Columbia University,New York University (NYU),Princeton University
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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