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STEM Bytes Seminar

April 12 @ 12:00 pm - 1:00 pm

Up next on STEM Bytes are two exciting seminars on Friday, April 12 from 12 – 1pm on Zoom! To register, Meeting Registration – Zoom

๐’๐ฉ๐ž๐š๐ค๐ž๐ซ: ๐‰๐ž๐ง๐ง๐ฒ ๐‚๐ก๐ž๐ฎ๐ง๐ 

๐ŸŽค Talk: Inferring Ecological Processes from Biodiversity Data Using Bayesian Inference Methods

Metacommunity ecology integrates local and regional dynamics to understand how processes across scales shape community assembly within broader ecological frameworks. However, interpreting metacommunity processes accurately and efficiently from spatiotemporal biodiversity data remains challenging because metacommunity models have many free parameters. One potential solution to this challenge is to use Approximate Bayesian Computation (ABC). Due to its computational intensity and implementation challenges, the empirical application of ABC on biodiversity data is seldom demonstrated. In my study, I leverage ABC and Random Forests, two statistical techniques that complement each other regarding accuracy and scalability, to demonstrate how one can estimate parameters involved in metacommunity processes from time series data.

๐’๐ฉ๐ž๐š๐ค๐ž๐ซ: ๐‡๐š๐ฒ๐ฅ๐ž๐ฒ ๐’๐จ๐ง๐ 

๐ŸŽค Talk: ManiFPT: On Defining and Analyzing Fingerprints of Generative Models

Recent works have shown that generative models leave traces of their underlying generative process on the generated samples, broadly referred to as fingerprints of a generative model, and have studied their utility in detecting synthetic images from real ones. However, the extend to which these fingerprints can distinguish between various types of synthetic image and help identify the underlying generative process remain under-explored. In particular, the very definition of a fingerprint remains unclear, to our knowledge. To that end, in this work, we formalize the definition of artifact and fingerprint in generative models, propose an algorithm for computing them in practice, and finally study its effectiveness in distinguishing a large array of different generative models.

๐™‰๐™ค๐™ฉ๐™š: ๐™๐™๐™ž๐™จ ๐™š๐™ซ๐™š๐™ฃ๐™ฉ ๐™ž๐™จ ๐™ค๐™ฅ๐™š๐™ฃ ๐™ฉ๐™ค ๐™ฉ๐™๐™š ๐™๐™Ž๐˜พ ๐™˜๐™ค๐™ข๐™ข๐™ช๐™ฃ๐™ž๐™ฉ๐™ฎ


April 12
12:00 pm - 1:00 pm