AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 2 of 9 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.
This work trains small language models from scratch on representative subsets of pretraining data and introduces variants of transformers in which standard activation functions are replaced with learned splines tuned on specific datasets, providing a simple way to modify their inductive biases.
The results show that standard transformers are rarely a local optimum in the space of architectures, and suggests that there may be room for improved architectures that better support multiple capabilities simultaneously, such as fluency and robust reasoning.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.