Journal club;
machine learning;
scientific computing.

Deschutes Hall, University of Oregon, Eugene, Oregon
Exact location floating; 3pm; Thursday (new time).

Organizers

Zac Cross — zacc†
Mathew Huerta-Enochian — mhuertae†
Joseph McLaughlin — jmclaug2†
† @uoregon.edu

2026.04.02 (Week 1s26)

Presenter — Eric Zander

2026.03.12 (Week 10w26)

Presenter — Said Efendiyev

2026.03.05 (Week 9w26)

Topic — RenderFlow: Single-Step Neural Rendering via Flow Matching
Presenter — Manish Mathai

2026.02.26 (Week 8w26)

Topic — FLOWER: A Flow-Matching Solver for Inverse Problems
Presenter — Zac Cross
Talk — PDF
In attendance — Simon Casey; Zac Cross; Said Efendiyev; Kai Iverson; Mathew Huerta-Enochian; Manish Mathai; Joseph McLaughlin; Tamanna Saini; Eric Zander.

2026.02.20 (Week 7w26)

Topic — Flow Matching Primer (a) (b) (c)
Presenter — Mathew Huerta-Enochian
Talk —
About — These three papers form much of the foundational theory of FM. If you can read all or some of them, please do so! If your time is constrained, we recommend reading them in the listed order.
In attendance — Zac Cross; Michael Dushkoff; Said Efendiyev; Mathew Huerta-Enochian; Manish Mathai; Joseph McLaughlin; Tamanna Saini; Eric Zander; Yiyang Zhang.
Remarks — MH: Standard manifold hypothesis is key assumption of FM, does this actually hold? MM: How did the authors generate the same images in Lipman? Overfitting on a small dataset? (b) does a diffusion-like operation to straighten the flows to more closely approximate OT (note that we are still working within the SMH here). The rationale for straighter flows is faster generation. (c) OT mini batches to learn OT over the whole model. MH: Octagauss? OT fields can cross as long as they do not cross at the same time. MD: why doesn't (c) do a quantitive analysis on the manifold? TS: Greedy vs stochastic OT. MD: Are there limitations when emerge when the problem is high dimensional? (Maybe). EZ: Non-gaussian sources? Varying the gaussian has an impact. Could you do transfer learning this way? TS: applications of greedy OT vs stochastic. MM: How robust is FM to changes in the source? Example application - denoising. JM: does the SMH make sense? Even if the objective is lower-dimension, is it strictly continuous? MH: This is why CNFs are cool(er than FM) because they don't assume the SMH.

2026.02.13 (Week 6w26)

Topic — DiffusionSat (2024)
Presenter — Michael Dushkoff
Talk — PDF
In attendance — Zac Cross; Michael Dushkoff; Said Efendiyev; Mathew Huerta-Enochian; Hritvik Jekki Venkateshwarulu; Manish Mathai; Joseph McLaughlin; Eric Zander.
Comments — Stable diffusion trained on clip with a control net. Would this generalize or is this paper just an impressive demonstration? Interesting enough to try in Michael's applied microscope tachography research.

Past papers from an even less organized time.

Journal club; machine learning; scientific computing.

Journal club;
machine learning;
scientific computing.