[CMU AI Seminar] April 13 (Today!) at 12pm (GHC 6115 & Zoom) -- Mazda Moayeri (UMD) -- Turning Models into Super Models without Supersizing: Making the most of what we already have -- AI Seminar sponsored by SambaNova Systems

[Asher Trockman]

Reminder this is happening now. There's pizza.

On Thu, Apr 13, 2023 at 8:06 AM Asher Trockman <ashert at cs.cmu.edu> wrote:

> Dear all,
>
> We look forward to seeing you* today, **this Thursday (4/13)* from *1**2:00-1:00
> PM (U.S. Eastern time)* for the next talk of this semester's
> *CMU AI Seminar*, sponsored by SambaNova Systems <https://sambanova.ai/>.
> The seminar will be held in GHC 6115 *with pizza provided *and will be
> streamed on Zoom. *Note: the speaker will be remote.*
>
> To learn more about the seminar series or to see the future schedule,
> please visit the seminar website <http://www.cs.cmu.edu/~aiseminar/>.
>
> Today (4/13), *Mazda Moayeri* (UMD) will be giving a talk titled *"**Turning
> Models into Super Models without Supersizing: Making the most of what we
> already have**".*
>
> *Title*: Turning Models into Super Models without Supersizing: Making the
> most of what we already have
>
> *Talk Abstract*: Newer, larger vision models trained on more data are
> released nearly every week. However, many critical issues persist, such as
> poor interpretability, robustness, and fairness. Today, we ask, how can we
> better use the models and data we already have to tackle these issues?
> First, we propose a method to organize data for improved spurious
> correlation robustness: We utilize an adversarially trained model to
> discover spurious features that models rely upon, and scalably measure the
> presence of these spurious cues (i.e. spuriosity) per image. After ranking
> images by spuriosity, we can very easily measure and mitigate bias caused
> by spurious correlations, all without needing new data. We demonstrate the
> feasibility of our framework on ImageNet, resulting in a massive dataset (
> salient-imagenet.cs.umd.edu) answering the question, “what reasons do
> deep models use to solve ImageNet classification?”. Next, we show how
> existing models can work together with minimal additional training. Namely,
> we present a method for accessing the feature space of an off-the-shelf
> vision models directly with text, extending multimodal (i.e. CLIP)
> capabilities to smaller unimodal models trained with far less data and
> supervision. Our method unlocks many new powers (especially for
> interpretability) of existing models, all without ever needing to change
> the model. For example, we show how after training just one linear layer,
> we can use a basic ResNet to retrieve images using text, diagnose
> distribution shifts w.r.t. human concepts, and even perform zero-shot
> classification nearly on par with CLIP.
>
> *Speaker Bio:* Mazda Moayeri is a third year PhD student in the Computer
> Science Department at the University of Maryland, advised by Dr. Soheil
> Feizi. His research focuses on building practical, efficient methods to
> improve the reliability and trustworthiness of AI. Having worked on
> adversarial and distributional robustness, his work now combines
> interpretability with robustness to diagnose distribution shifts and tailor
> mitigation strategies specific to the uncovered vulnerabilities. He is
> supported by the ARCS foundation as their Endowment Scholar and will be
> hosted by FAIR in the summer.
>
> *In person: *GHC 6115
> *Zoom Link*:
> https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09
>
> Thanks,
> Asher Trockman
>
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