Achieving such guarantees generally requires the injection of noise, either directly into parameter estimates or into the estimation process.

Neural image compression has made a great deal of progress. State-of-the-art models are based on variational autoencoders and are outperforming classical models.

Large language models (LLMs) have demonstrated remarkable capabilities in understanding and generating natural language data.

Suppose h ( )=e x p ( ) . M -matrix (see [ 50 ]).

Our framework applies to a wide range of SMF-type problems for multi-class classification with auxiliary features.