Update README to include more implemented models

README.md

@@ -21,6 +21,7 @@ We implement both linear and log-complexity recurrent models.
 | Linear Recurrent Neural Network | $O(\log{n})$ | [[paper]](https://arxiv.org/abs/1709.04057) | [[code]](memax/equinox/semigroups/lrnn.py) |
 | Fast Autoregressive Transformer | $O(\log{n})$ | [[paper]](https://arxiv.org/abs/2006.16236) | [[code]](memax/equinox/semigroups/fart.py) |
 | Fast and Forgetful Memory | $O(\log{n})$ | [[paper]](https://arxiv.org/abs/2310.04128) | [[code]](memax/equinox/semigroups/ffm.py) |
+| Frame Stacking | $O(\log{n})$ | [[paper]](https://arxiv.org/abs/1312.5602) | [[code]](memax/equinox/semigroups/stack.py) |
 | Rotational RNN (RotRNN) | $O(\log{n})$ | [[paper]](https://arxiv.org/abs/2407.07239) | [[code]](memax/equinox/semigroups/spherical.py) |
 | Fast Weight Programmer | $O(\log{n})$ | [[paper]](https://arxiv.org/pdf/2508.08435) | [[code]](memax/equinox/semigroups/fwp.py) |
 | DeltaNet | $O(\log{n})$ | [[paper]](https://arxiv.org/pdf/2406.06484) | [[code]](memax/equinox/semigroups/delta.py) |
@@ -32,6 +33,7 @@ We implement both linear and log-complexity recurrent models.
 | ALiBi-Attention | $O(\log{n})$ | [[paper]](https://arxiv.org/abs/2108.12409) | [[code]](memax/equinox/semigroups/attn.py) |
 | Elman Network | $O(n)$ | [[paper]](https://www.sciencedirect.com/science/article/pii/036402139090002E) | [[code]](memax/equinox/set_actions/elman.py) |
 | Gated Recurrent Unit | $O(n)$ | [[paper]](https://arxiv.org/abs/1412.3555) | [[code]](memax/equinox/set_actions/gru.py) |
+| Independently Recurrent Neural Network | $O(n)$ | [[paper]](https://arxiv.org/abs/1803.04831) | [[code]](memax/equinox/set_actions/indrnn.py) |
 | Minimal Gated Unit | $O(n)$ | [[paper]](https://arxiv.org/abs/1603.09420) | [[code]](memax/equinox/set_actions/mgu.py) |
 | Long Short-Term Memory Unit | $O(n)$ | [[paper]](https://ieeexplore.ieee.org/abstract/document/6795963) | [[code]](memax/equinox/set_actions/lstm.py) |