[Eoas-seminar] [Seminar-announce] Scientific Computing Colloquium with Sandeep Nagar

[eoas-seminar at lists.fsu.edu]

"Efficient Normalizing Flows: Theory, Algorithms, and Applications"

Dr. Sandeep Nagar
Institute for Advanced Study, Technical University of Munich

Please feel free to forward/share this invitation with other groups/disciplines that might be interested in this talk/topic. All are welcome to attend.

NOTE: In-person attendance is requested in our 499 Dirac Science Library (DSL) Seminar Room. Zoom access is intended for external (non-departmental) participants only.

https://fsu.zoom.us/j/94273595552
Meeting # 942 7359 5552

🎦 Colloquium recordings will be made available here, https://www.sc.fsu.edu/colloquium<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.sc.fsu.edu%2Fcolloquium&data=05%7C02%7Csc-seminar-announce%40lists.fsu.edu%7C01a027cdd135496f095c08de73a9dfa7%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C639075370584144355%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=ntBVM8QU9p54j%2B5v%2FhNdtV1PZeZHWO%2FsK%2Fspljldcwc%3D&reserved=0>


Wednesday, Feb 25, 2026, Schedule:

* 3:00 to 3:30 PM Eastern Time (US and Canada)
☕ Nespresso & Teatime - 417 DSL Commons

* 3:30 to 4:30 PM Eastern Time (US and Canada)
🕟 Colloquium - 499 DSL Seminar Room


Abstract:

In this talk, I will discuss research that advances both the theoretical foundations and practical efficiency of Normalizing Flows, while also demonstrating their impact on computer vision tasks. We will discuss how to improve the efficiency and scalability of flow-based generative models. I will introduce a series of architectural and algorithmic, including mathematically grounded conditions for invertible 3×3 convolutional layers, a more expressive and efficient quad-coupling layer, and a fast parallel algorithm for general k×k inverse convolutions. Building on this, I will present an efficient backpropagation algorithm for the inverse of convolution, enabling a new training paradigm, Inverse-Flow, in which the inverse-of-convolution operation is used in the forward pass and convolution is used in the sampling pass. Together, these significantly reduce computational overhead for training and sampling while preserving exact invertibility and likelihood-based training.


Additional colloquium details can be found here,
https://www.sc.fsu.edu/news-and-events/colloquium/1907-colloquium-with-sandeep-nagar-2026-02-25<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.sc.fsu.edu%2Fnews-and-events%2Fcolloquium%2F1907-colloquium-with-sandeep-nagar-2026-02-25&data=05%7C02%7Csc-seminar-announce%40lists.fsu.edu%7C01a027cdd135496f095c08de73a9dfa7%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C639075370584192320%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=qtSAiWZkvE0RUOQ%2FG7rpVg0o5kiRkaIeHnebR%2BPTIPg%3D&reserved=0>

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