论文标题
张量轨道VII：从量子重力到人工智能
The Tensor Track VII: From Quantum Gravity to Artificial Intelligence
论文作者
论文摘要
假设对量子场理论有所熟悉，并且对我们中的一个在上一系列张量I到VI中提出的张量轨道方法，我们像往常一样提供了过去两年中量子重力的发展。接下来，我们详细介绍两种灵感来自随机张量理论的算法，该算法是在量子重力环境中开发的。一个人专门用于在随机张量中检测和恢复信号，这可能与噪声相关联，并为更一般的情况（例如具有不同尺寸的张量）提供了新的理论保证。另一个是SMPI，更雄心勃勃，但也许不那么严格。它致力于显着，从根本上提高算法的性能进行张量的主成分分析，但尚无完整的理论保证。然后，我们绘制与信息理论和人工智能相关的各种应用程序，并提供相应的书目。
Assuming some familiarity with quantum field theory and with the tensor track approach that one of us presented in the previous series Tensor Track I to VI, we provide, as usual, the developments in quantum gravity of the last two years. Next we present in some detail two algorithms inspired by Random Tensor Theory which has been developed in the quantum gravity context. One is devoted to the detection and recovery of a signal in a random tensor, that can be associated to the noise, with new theoretical guarantees for more general cases such as tensors with different dimensions. The other, SMPI, is more ambitious but maybe less rigorous. It is devoted to significantly and fundamentally improve the performance of algorithms for Tensor principal component analysis but without complete theoretical guarantees yet. Then we sketch all sorts of application relevant to information theory and artificial intelligence and provide their corresponding bibliography.
