Scott Mahan

According to our database, Scott Mahan authored at least 10 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Saddle-Free Guidance: Improved On-Manifold Sampling without Labels or Additional Training.

Eric Yeats, Darryl Hannan, Wilson Fearn, Timothy Doster, Henry Kvinge, Scott Mahan

CoRR, November, 2025

A Connection Between Score Matching and Local Intrinsic Dimension.

Eric Yeats, Aaron Jacobson, Darryl Hannan, Yiran Jia, Timothy Doster, Henry Kvinge, Scott Mahan

CoRR, October, 2025

Automating Evaluation of Diffusion Model Unlearning with (Vision-) Language Model World Knowledge.

Eric Yeats, Darryl Hannan, Henry Kvinge, Timothy Doster, Scott Mahan

CoRR, July, 2025

2024

Point Cloud Classification via Deep Set Linearized Optimal Transport.

Scott Mahan, Caroline Moosmüller, Alexander Cloninger

CoRR, 2024

ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain.

Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna, Federica Baccini, Mathilde Papillon, Miquel Ferriol-Galmés, Mustafa Hajij, Theodore Papamarkou, Maria Sofia Bucarelli, Olga Zaghen, Johan Mathe, Audun Myers, Scott Mahan, Hansen Lillemark, Sharvaree Vadgama, Erik J. Bekkers, Tim Doster, Tegan Emerson, Henry Kvinge, Katrina Agate, Nesreen K. Ahmed, Pengfei Bai, Michael Banf, Claudio Battiloro, Maxim Beketov, Paul Bogdan, Martin Carrasco, Andrea Cavallo, Yun Young Choi, George Dasoulas, Matous Elphick, Giordan Escalona, Dominik Filipiak, Halley Fritze, Thomas Gebhart, Manel Gil-Sorribes, Salvish Goomanee, Victor Guallar, Liliya Imasheva, Andrei Irimia, Hongwei Jin, Graham Johnson, Nikos Kanakaris, Boshko Koloski, Veljko Kovac, Manuel Lecha, Minho Lee, Pierrick Leroy, Theodore Long, German Magai, Alvaro Martinez, Marissa Masden, Sebastian Meznar, Bertran Miquel-Oliver, Alexis Molina, Alexander Nikitin, Marco Nurisso, Matt Piekenbrock, Yu Qin, Patryk Rygiel, Alessandro Salatiello, Max Schattauer, Pavel Snopov, Julian Suk, Valentina Sánchez, Mauricio Tec, Francesco Vaccarino, Jonas Verhellen, Frédéric Wantiez, Alexander Weers, Patrik Zajec, Blaz Skrlj, Nina Miolane

Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM) at ICML 2024, 2024

2022

Semi-Supervised Manifold Learning with Complexity Decoupled Chart Autoencoders.

Stefan C. Schonsheck, Scott Mahan, Timo Klock, Alexander Cloninger, Rongjie Lai

CoRR, 2022

2021

Nonclosedness of sets of neural networks in Sobolev spaces.

Scott Mahan, Emily J. King, Alex Cloninger

Neural Networks, 2021

DNA: Dynamic Network Augmentation.

Scott Mahan, Tim Doster, Henry Kvinge

CoRR, 2021

Rotating spiders and reflecting dogs: a class conditional approach to learning data augmentation distributions.

Scott Mahan, Henry Kvinge, Tim Doster

CoRR, 2021

2020

Nonclosedness of the Set of Neural Networks in Sobolev Space.

Scott Mahan, Emily J. King, Alex Cloninger

CoRR, 2020