LLM4All is an ANR project dedicated to finetuning LLMs to ensure that they will stay up-to-date. Two application domains are considered: meetings understanding, and emergency calls for hospitals. For more info, see the project website.
We are developping inference and training scripts, with the help of CNRS GENCI AI engineers, to deploy on the Jean Zay cluster to help the community starting on Jean Zay with giga-models. For more info, see our website, our PLM4All gitter and our PLM4All git.
Proposal of a novel unsupervised loss function that provably converges towards the optimal classifier risk and improves the generalization properties of binary deep learning classificiation models. Research paper on HAL
Galaxy detection with Bayesian Neural Networks. Research paper on HAL
Study of the geometrical properties of the loss landscape of growing neural networks: we show that optima are flatter, and improve generalization performances. Research paper on HAL