While true in a theoretical sense (an MLP of sufficient size can theoretically represent any differentiable function), in practice it’s often the case that it’s impossible for a certain architecture to learn a specific task no matter how much compute you throw at it. E.g. an LSTM will never capture long range dependencies that a transformer could trivially learn, due to gradients vanishing after a certain sequence length.
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While true in a theoretical sense (an MLP of sufficient size can theoretically represent any differentiable function), in practice it’s often the case that it’s impossible for a certain architecture to learn a specific task no matter how much compute you throw at it. E.g. an LSTM will never capture long range dependencies that a transformer could trivially learn, due to gradients vanishing after a certain sequence length.
You are right with respect to ordering of operations, where recurrent networks have a whole bunch of other computational complexity to them.
However, for example, a Transformer can be represented with just deeply connected layers, albeit with a lot of zeros for weights.