Multifidelity optimization can inform decision-making during process development and reduce the number of experiments ...
where \(\mathsf{G}(\cdot)\) is some convex operator and \(\mathcal{F}\) is as set of feasible input distributions. Examples of such an optimization problem include finding capacity in information ...
In the first instalment of LCGC International's interview series exploring how artificial intelligence (AI)/machine learning ...
Rajalakshmi Srinivasaraghavan highlights hybrid AI compiler and runtime optimizations balancing CPUs, GPUs, and accelerators.
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