[CMU AI Seminar] Special! February 23 at 2pm (NSH 3305 & Zoom) -- Ludwig Schmidt (U. Washington) -- A data-centric view on reliable generalization: From ImageNet to LAION-5B -- AI Seminar sponsored by SambaNova Systems

[Asher Trockman]

Reminder this is happening at 2pm in NSH 3305!

On Tue, Feb 21, 2023 at 4:31 PM Asher Trockman <ashert at cs.cmu.edu> wrote:

> Dear all,
>
> We look forward to seeing you *this Thursday (2/23)* from *2:00-3:00 PM
> (U.S. Eastern time)* for a special installment of this semester's
> *CMU AI Seminar*, sponsored by SambaNova Systems <https://sambanova.ai/>.
> The seminar will be held in *NSH 3305* with* pizza provided *and will be
> streamed on Zoom. *Note:* The speaker will be *in person*.
>
> To learn more about the seminar series or to see the future schedule,
> please visit the seminar website <http://www.cs.cmu.edu/~aiseminar/>.
>
> This Thursday (2/23), *Ludwig Schmidt* (University of Washington) will be
> giving a talk titled *"**A data-centric view on reliable generalization:
> From ImageNet to LAION-5B**".*
>
> *Title*: A data-centric view on reliable generalization: From ImageNet to
> LAION-5B
>
> *Talk Abstract*: Researchers have proposed many methods to make neural
> networks more reliable under distribution shift, yet there is still large
> room for improvement. Are better training algorithms or training data the
> more promising way forward? In this talk, we study this question in the
> context of OpenAI’s CLIP model for learning from image-text data.
>
> First, we survey the current robustness landscape based on a large-scale
> experimental study involving more than 200 different models and test
> conditions. The CLIP models stand out with unprecedented robustness on
> multiple challenging distribution shifts. To further improve CLIP, we then
> introduce new methods for reliably fine-tuning models by interpolating the
> weights of multiple models. Next, we investigate the cause of CLIP’s
> robustness via controlled experiments to disentangle the influence of
> language supervision and training distribution. While CLIP leveraged large
> scale language supervision for the first time, its robustness actually
> comes from the pre-training dataset.
>
> We conclude with a brief overview of ongoing work to improve pre-training
> datasets: LAION-5B, the largest public image-text dataset, and initial
> experiments to increase the robustness induced by pre-training data.
>
> *Speaker Bio:* Ludwig Schmidt is an assistant professor in the Paul G.
> Allen School of Computer Science & Engineering at the University of
> Washington. Ludwig’s research interests revolve around the empirical
> foundations of machine learning, often with a focus on datasets, reliable
> generalization, and large models. Ludwig completed his PhD at MIT under the
> supervision of Piotr Indyk and was a postdoc at UC Berkeley hosted by
> Benjamin Recht and Moritz Hardt. Recently, Ludwig’s research group
> contributed to multimodal language & vision models by creating OpenCLIP and
> the LAION-5B dataset. Ludwig’s research received a new horizons award at
> EAAMO, best paper awards at ICML & NeurIPS, a best paper finalist at CVPR,
> and the Sprowls dissertation award from MIT.
>
> *In person: *NSH 3305
> *Zoom Link*:
> https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09
>
> Thanks,
> Asher Trockman
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/ai-seminar-announce/attachments/20230223/c611b2ce/attachment.html>