I've been looking at non-Euclidean loss functions in my PhD, and particularly at ultrametric loss functions. That gives you a very different kind of supervised learning (even if you are just doing linear regression). And it may be relevant here because it's very good at modelling hierarchies (e.g. ancestry).
So if you interpret 'different kind of architecture' as 'we need to do something other than what we're doing at the moment with Euclidean-based linear regression' then I agree with the post, but if it's 'we must do deep learning with neural networks' then I agree with Yejun.
I've been looking at non-Euclidean loss functions in my PhD, and particularly at ultrametric loss functions. That gives you a very different kind of supervised learning (even if you are just doing linear regression). And it may be relevant here because it's very good at modelling hierarchies (e.g. ancestry).
So if you interpret 'different kind of architecture' as 'we need to do something other than what we're doing at the moment with Euclidean-based linear regression' then I agree with the post, but if it's 'we must do deep learning with neural networks' then I agree with Yejun.