AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 2 of 2 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
In OpenWebText2 language modelling, LSTM-UT outperforms CoTFormer and, at equal width, reaches slightly lower perplexity than CoTFormer while CoTFormer needs up to 91% more training time per step; against a parameter-matched CoTFormer, LSTM-UT comes within 0.6 perplexity.
Recurrent-depth Transformers apply shared computation repeatedly, but differ in how they retain information across steps. We compare a Block Universal Transformer (BUT), which carries only its current hidden state; CoTFormer, which also retains an expanding attention cache; and a new LSTM Universal Transformer (LSTM-UT...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.