3 papers indexed here
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
This article provides a gentle introduction to recent advances in deep learning from a control theory perspective and discusses how control theory has recently been used to better understand and design deep neural networks.
It is shown that the effective required data ratio to prevent model collapse is different than previously implied, and quantitative contraction and invariance bounds that are stable and do not become trivial as the dimensions increase are derived.
It is shown that the resemblance between SSMs and transformers does run deeper: the recurrence at the core of SSMs aggregates tokens just as attention does, and the evolution of tokens across layers as an ordinary differential equation.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
