AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
3 papers indexed here
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.
E3j, a fast Euclid-equivariance backend for geometric deep learning applications with JAX bindings for GPU and TPU architectures, is presented, which opens the possibility of large scale equivariant deep learning workloads on TPU architectures, which has so far been difficult to achieve.
Machine learning for scientific discovery is almost systematically data bound. Producing relevant high quality data, under budget constraints, is amongst the most promising ways to advance the field. Active learning (AL) offers promise wherever labelling requires expensive experiment, measurement, or simulation. Most e...
Training set quality and diversity are key determinants of the reliability of machine learning interatomic potentials (MLIPs), yet using massive datasets in full is often impractical and redundant, making intelligent data selection essential. A major bottleneck, however, is the lack of infrastructure for uniformly acce...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.