Research
58d agoMatrix Orthogonalization Boosts Recurrent Model Memory
Orthogonal weight matrices prevent gradient explosion and vanish in recurrent networks. This technique stabilizes training by maintaining the norm of the hidden state over long sequences. Matrix Orthogonalization allows models to retain information across more time steps.
The Signal
Practitioners can now implement more stable long-term dependencies without relying solely on LSTM or GRU gating mechanisms.