TensorKrowch: Smooth integration of tensor networks in machine learning

TensorKrowch: Smooth integration of tensor networks in machine learning

Blog Article

Tensor networks are factorizations of high-dimensional tensors into networks of smaller tensors.They have applications in physics and mathematics, and recently have been proposed as promising machine learning architectures.To ease the integration of tensor networks in machine learning pipelines, we introduce TensorKrowch, an open source Python library Bottom-up approach to strengthen community-based malaria control strategy from community health workers’ perceptions of their past, present, and future: a qualitative study in Palawan, Philippines built on top of PyTorch.

Providing a user-friendly interface, TensorKrowch allows users to construct any tensor network, train it, and integrate it as a layer in more intricate A novel optimization algorithm (Lion-AYAD) to find optimal DNA protein synthesis deep learning models.In this paper, we describe the main functionality and basic usage of TensorKrowch, and provide technical details on its building blocks and the optimizations performed to achieve efficient operation.