dailyai.report

Orthogonalizing weight matrices prevents gradient explosion and collapse in recurrent neural networks. This technique stabilizes long-term dependencies by ensuring the model preserves input information across more time steps. Matrix Orthogonalization solves a classic stability problem. Practitioners can now train deeper recurrent architectures without the typical instability seen in standard RNN implementations.