Machine learning-driven carrier risk modeling enables supply chains to predict and prevent pickup defects, reducing costs and improving on-time performance.

In this work, we extend diffusion solvers to efficiently handle general noisy (non)linear inverse problems via the approximation of the posterior sampling. Interestingly, the resulting posterior ...

A high-level module for Keras-like training with callbacks, constraints, and regularizers. Comprehensive data augmentation, transforms, sampling, and loading Utility tensor and variable functions so ...