Multifidelity optimization can inform decision-making during process development and reduce the number of experiments ...
Sahil Dua discusses the critical role of embedding models in powering search and RAG applications at scale. He explains the ...
Researchers at Santa Clara University have developed a comprehensive mathematical framework to rigorously analyze and predict ...
Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with pseudo-inverse training implemented using JavaScript. Compared to other training techniques, such as ...
Quantum physics may sound abstract, but Ph.D. candidates Kirsten Kanneworff and David Dechant show that quantum research can also be very concrete. Together, they are investigating how quantum ...
Researchers have developed a new machine-learning-assisted approach to optimize micro-electro-discharge machining (µ-EDM) of ...
Abstract: We study reinforcement learning with linear function approximation and finite-memory approximations for partially observed Markov decision processes (POMDPs). We first present an algorithm ...
Abstract: This article presents and applies an electromagnetic-thermal model to design a double-sided coreless, conduction-cooled high temperature-superconductor (HTS) linear motor for a high-dynamic ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results