dailyai.report

Orthogonalizing weight matrices prevents gradient explosion and decay in recurrent neural networks. This technique stabilizes training by maintaining the norm of the hidden state over long sequences. Matrix Orthogonalization allows models to retain information across larger temporal gaps. Practitioners can now implement more stable long-term dependencies without relying solely on LSTM gating mechanisms.