Quantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and ...
Aiming at the optimization of public sports service quality, this study analyzes the public sports service data deeply by constructing a supervised learning model. Firstly, the theoretical framework ...
AI thrives on data but feeding it the right data is harder than it seems. As enterprises scale their AI initiatives, they face the challenge of managing diverse data pipelines, ensuring proximity to ...
Sedai, the self-driving cloud™, today launched AI Agent Optimization: the first platform that autonomously optimizes the cost, performance, and accuracy of running AI agents. The platform gives ...
Pruna AI, a European startup that has been working on compression algorithms for AI models, is making its optimization framework open source on Thursday. Pruna AI has been creating a framework that ...
Two papers on MoE-specific quantization algorithms accepted at a workshop held in conjunction with ICML 2026Recognition ...
Microsoft's SkillOpt brings deep-learning discipline to AI agent skills, replacing manual prompt tweaking with mathematically ...
Researchers at the University of Illinois Urbana-Champaign and the University of Virginia have developed a new model architecture that could lead to more robust AI systems with more powerful reasoning ...
As enterprise AI adoption enters the multi-model era, cost efficiency, performance, reliability, and governance have become ...
In this article, as in industry, advanced process control (APC) refers primarily to multi-variable control. Multivariable control means adjusting multiple single-loop controllers in unison, to meet ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results