Well ish. The article above explains that Recursive-NNs are hierarchical whereas RNNs are linear. I guess the distinction is a little on the fine side.
The recursive neural networks described there are a failed academic project from more than a decade ago, predating modern deep learning. Basically everyone using the phrase recursive nn nowadays is probably just mispeaking for RNN. RNNs also are not linear
I don't know about "everybody nowadays" but I remember Recursive Neural Nets as an architecture introduced by Christopher Manning with the argument that it was better suited to the hierarchical structure of language than existing architectures. I did find it a bit of a bad choice of name, given that it's so closed to Recurrent Neural Nets. All this is from memory though I might check the internets later to see what I misremember.
RNNs are a large class of architectures of varying complexity, from Kallman Filters to LSTMs. It's not clear to me exactly what the wikipedia article means by "linear" but LSTMs for example treat their inputs as sequences and don't try to deconstruct them into parts, like e.g. Convolutional Neural Nets do. So maybe that's what's meant by "linear".
No opinion on the specifics of this distinction, but it's worth noting that in research, an awful lot of successful projects have their origins in failed projects of decades ago...
My experience working in machine learning academia is an overfocus on failed projects from the early 00s to 90s that really only stopped in 2020+.
We can often trace back successful projects to failed precursors, but often the people behind the successful project are not even familiar with the failed precursor and the 'connection to the past' only really occurs in retrospect. See the 'adjoint state method' and connections with backprop.
This is sometimes true, sure. And often the older work has more entered the general consciousness than being chased down by searching specific cites. On the other hand, very little is truly new, and recency bias can lead you into all sorts of back-eddy's.
Once the dust has settled, there are often much clearer through lines than in looked like at the time. It's hard to see when you are on the moving front though.
That is exactly how LLM inference is performed, so I'm being cheeky (I'm 99% sure anyone proposing anything in this thread is someone handwaving based on limited understanding)
To playfully invite for you to participate in conversation further, so that I may humbly learn from you. "I don't know what you're talking about" seemed too spartan and austere and aggressive, and you reciprocated politely, if again sparsely, when the other person playfully invited you to elaborate.
Well, you've now made your original intent specific, but in case you didn't draw the requisite lesson I'll make that explicit.
Because text has less bandwidth than almost any other medium, certain forms of humor are much more likely to be understood (in this case, your "gentle playfulness" was taken to be snark, sarcasm, and point scoring).
If you insist on using this and similar forms of humor that, ordinarily, depend quite strongly on intonation to convey intent, you'll have to be much more explicit to avoid being misunderstood. You are going to have actually state your intent explicitly as part of your communication. This need not entirely destroy the humor, for example, you might try something like this:
And so I say to you (playfully, sir, playfully): etc.
Or this:
Yadda yadda yadda. (I kid, I kid!)
The Internet-native forms of this are the humble ;-) or the newer j/k, but I find that it is all too easy to overlook a 3-character sequence, particularly if the passage being so marked is even as long as a single paragraph, but they can serve their purpose when used for the commonplace one-liner.
https://en.wikipedia.org/wiki/Recursive_neural_network
Well ish. The article above explains that Recursive-NNs are hierarchical whereas RNNs are linear. I guess the distinction is a little on the fine side.
Anyway carry on. Pedantic moment over.