• Lipschitz RNN (--model LipschitzRNN)
• Antisymmetric RNN (--model asymRNN)
• Cayley RNN (--model calRNN)
• Residual RNN (--model resRNN)
• Vanilla RNN: (--model RNN)

Here is an example ro run the Lipschitz RNN on the ordered pixel-by-pixel MNIST classification task:

python3 driver.py --name mnist --T 784 --model LipschitzRNN --n_units 128 --epochs 90 --eps 0.01 --lr 0.1 --lr_decay 0.2 --lr_decay_epoch 30 60 80 --beta 0.65 --init_std 32 --gamma 0.001

Here is an example ro run the Lipschitz RNN on the permuted pixel-by-pixel MNIST classification task:

python3 driver.py --name pmnist --T 784 --model LipschitzRNN --n_units 128 --epochs 90 --eps 0.01 --lr 0.1 --lr_decay 0.2 --lr_decay_epoch 30 60 80 --beta 0.8 --init_std 32 --gamma 0.001

https://arxiv.org/pdf/2006.12070.pdf