[CMU AI Seminar] May 10 at 12pm (Zoom) -- Albert Gu (Stanford) -- Efficiently Modeling Long Sequences with Structured State Spaces -- AI Seminar sponsored by Morgan Stanley

[Asher Trockman]

Hi all,

Just a reminder that Albert will be giving his talk on "Efficiently
Modeling Long Sequences with Structured State Spaces" in 10 minutes. Zoom:
https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09

Thanks,
Asher


On Mon, May 9, 2022 at 11:31 AM Asher Trockman <ashert at cs.cmu.edu> wrote:

> Dear all,
>
> We look forward to seeing you *tomorrow, this Tuesday (5/10)* from *1**2:00-1:00
> PM (U.S. Eastern time)* for the next talk of our *CMU AI seminar*,
> sponsored by Morgan Stanley
> <https://www.morganstanley.com/about-us/technology/>.
>
> To learn more about the seminar series or see the future schedule, please
> visit the seminar website <http://www.cs.cmu.edu/~aiseminar/>.
>
> *Tomorrow* (5/10), *Albert Gu *(Stanford) will be giving a talk titled *"**Efficiently
> Modeling Long Sequences with Structured State Spaces**" *to share his
> work proposing the S4 model, which handles long-range dependencies
> mathematically and empirically, and can be computed very efficiently.
>
> *Title*: Efficiently Modeling Long Sequences with Structured State Spaces
>
> *Talk Abstract*: A central goal of sequence modeling is designing a
> single principled model that can address sequence data across a range of
> modalities and tasks, particularly on long-range dependencies.  Although
> conventional models including RNNs, CNNs, and Transformers have specialized
> variants for capturing long dependencies, they still struggle to scale to
> very long sequences of 10000 or more steps.  This talk introduces the
> Structured State Space sequence model (S4), a simple new model based on the
> fundamental state space representation $x'(t) = Ax(t) + Bu(t), y(t) = Cx(t)
> + Du(t)$. S4 combines elegant properties of state space models with the
> recent HiPPO theory of continuous-time memorization, resulting in a class
> of structured models that handles long-range dependencies mathematically
> and can be computed very efficiently. S4 achieves strong empirical results
> across a diverse range of established benchmarks, particularly for
> continuous signal data such as images, audio, and time series.
>
> *Speaker Bio*: Albert Gu is a final year Ph.D. candidate in the
> Department of Computer Science at Stanford University, advised by
> Christopher Ré. His research broadly studies structured representations for
> advancing the capabilities of machine learning and deep learning models,
> with focuses on structured linear algebra, non-Euclidean representations,
> and theory of sequence models. Previously, he completed a B.S. in
> Mathematics and Computer Science at Carnegie Mellon University.
>
> *Zoom Link*:
> https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09
>
> Thanks,
> Asher Trockman
>
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