Update README.md

README.md

@@ -56,8 +56,8 @@ Some highlights of the architecture:
 One of the advantages of deep signal processing models is their flexibility. Different parametrizations of convolutions can be used depending on the memory, expressivity and causality requirements of pretraining, finetuning or inference workloads. 
 
 The main classes are:
-- Modal: unconstrained poles ([reference](https://arxiv.org/pdf/2203.14343.pdf), [reference](https://arxiv.org/abs/2310.18780)), or constrained poles ([reference](https://arxiv.org/abs/2206.11893), [reference](https://arxiv.org/pdf/2303.06349.pdf)).
-- Canonical / Rational: TBA.
+- Modal canonical: unconstrained poles ([reference](https://arxiv.org/pdf/2203.14343.pdf), [reference](https://arxiv.org/abs/2310.18780)), or constrained poles ([reference](https://arxiv.org/abs/2206.11893), [reference](https://arxiv.org/pdf/2303.06349.pdf)).
+- Companion canonical / rational: TBA.
 - Hypernetworks: hypernetwork ([reference](https://arxiv.org/abs/2102.02611)), modulated hypernetwork ([reference](https://arxiv.org/abs/2302.10866)).
 - Explicit: modulated explicit ([reference](https://arxiv.org/pdf/2210.09298.pdf)).