[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]

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
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