• Jan  30,  2024: Website of workshop is published.
  • Jan  21,  2024: Workshop proposal is accepted.

Workshop Description

Tensor Networks (TNs), with deep roots in quantum physics, chemistry, and applied mathematics, have demonstrated exceptional performance in handling high-dimensional data, generating multiway structured data, optimizing neural network structures and etc. Recently, it is further developed as a potential driving force in advances of machine learning (ML) and artificial intelligence (AI), particularly in crucial areas such as quantum machine learning, trustworthy machine learning, and interpretable machine learning. This workshop aims to bring together researchers from multidisciplinary and discuss not only the fundamental challenges in TNs such as TN structure optimization, efficient algorithms, and robustness, but also their extended application in addressing key challenges within machine learning, spanning domains such as efficiency, interpretability, reliability and etc. These efforts not only advance a deeper understanding of machine learning models and enhance their reliability but also ensure they meet the demand for resource efficiency, thereby increasing trustworthiness. Furthermore, our workshop is expected to unleash the potential of TNs to open new directions for future research in quantum-inspired machine learning.

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