VASC Seminar - Eliahu Horwitz
— 4:30pm
Location:
In Person
-
Newell-Simon 3305
Speaker:
ELIAHU HORWITZ
,
Google Ph.D. Fellow in Machine Learning and ML Foundations
and, Ph.D. Candidate in Computer Science
Hebrew University of Jerusalem
https://horwitz.ai
Machine learning has transformed many fields by learning from large collections of data. Yet, it is rarely applied to its own outputs: the models themselves. Today, with millions of publicly available models, a natural question arises: what can we do with so many models? In this talk, I will motivate two core applications that leverage this untapped potential, demonstrating their utility in the context of computer vision: (i) identifying emerging trends in model design, and (ii) reducing the need to train models from scratch through model recycling. To support these goals, I introduce the Model Atlas: a structured graph that represents models, their attributes, and the weight-space transformations that interconnect them. My research into weight-space learning enables the construction of this atlas by treating models themselves as data and inferring properties such as functionality, performance, and lineage directly from their weights. I will present key observations and methodologies that make weight-space learning possible at scale. As a visual prelude, you can explore the repository under study.
—
Eliahu Horwitz is a Google PhD Fellow in Machine Learning and ML Foundations and a final-year PhD candidate in Computer Science at The Hebrew University of Jerusalem, advised by Prof. Yedid Hoshen. His research centers on learning representations of neural network weights and understanding model populations directly in weight space. He is particularly interested in how weight-space learning can enable new downstream capabilities, such as model forensics, model discovery, and interpretability, and in how treating models as data points can advance broader areas of machine learning. Eliahu is also a recipient of the Israeli Council for Higher Education Scholarship and has previously interned at Google Research.
The VASC seminar is generously sponsored by HeyGen
For More Information:
cdowney@andrew.cmu.edu