Tag: pytorch

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Released TanSacNet Pre-release20250105

We updated TanSacNet Project for developing tangent space adaptive control networks.

  • New PyTorch Implementation: Introduced a PyTorch-based 2-D LSUN framework, including batch processing in orthonormalTransform.py and a low-dimensional approximation sample. Optimized gradient computation and sequential matrix processing to improve efficiency in PyTorch and CUDA environments.
  • Enhanced MATLAB Support: Improved data type and device management to ensure stability and performance in MATLAB workflows.
  • Code Refactoring and Stability: Streamlined code structure for maintainability and resolved initialization and CPU processing issues across both frameworks.

This pre-release highlights the new PyTorch implementation, along with key updates to MATLAB support and overall code stability.

Paper Published in IAPSIPA Transactions on Signal and Information Processing

 

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Paper Published in IAPSIPA Transactions on Signal and Information Processing

The following paper has been published in APSIPA Transactions on Signal and Information Processing (Q1: Information Systems).

It is a result of Grant-in-Aid for Scientific Research (A) “(JP22H00512) Locally Structured Unitary Networks and Tangent Space Learning for Dynamic Systems Modeling”.

It is Open Access. The source code is available at Code Ocean.

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