2023-12-01 · preprint · Neural Networks and Applications · en
highly citedClaims drawn from cited facts, not live model generation.
This paper is a preprint focused on neural networks and applications, addressing sequence modeling through selective state spaces. As a preprint, it has not yet undergone formal peer review, so its findings should be treated as preliminary.
3 cited facts
This work is the product of a small team of two researchers.
1 cited fact
This work has been cited 1,044 times, a figure that marks it as an established reference point in its field. It references no prior works, with a reference count of 0.
3 cited facts
Mamba: Linear-Time Sequence Modeling with Selective State Spaces is a preprint, focusing on Neural Networks and Applications, published in 2023.
Mamba: Linear-Time Sequence Modeling with Selective State Spaces has 1,044 citations in our record, placing it among the highly cited works we track.
Our record lists 2 authors for Mamba: Linear-Time Sequence Modeling with Selective State Spaces. Author names aren't in our data yet — the source link on the page lists the full byline.
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