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Pacific Northwest Seminar on Topology, Algebra, and Geometry in Data Science
Past Events
- Graph Metanetworks for Processing Diverse Neural Architectures (Derek Lim (MIT)) - February 6, 2024
- Generalization and universality in deep learning (Florentin Guth (NYU)) - January 30, 2024
- Machine learning, toric Fano varieties, and terminal singularities (Sara Veneziale (Imperial College London)) - January 16, 2024
- Global optimization of analytic functions over compact domain (Georgy Scholten, Sorbonne Université) - November 7, 2023
- Towards a deeper theoretical understanding of neural networks (Lechao Xiao, Google Brain) - February 16, 2023
- A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions (Swati Padmanabhan, UW) - February 9, 2023
- What can adversarial examples tell us about similarities between neural networks? (Jacob Springer, Carnegie Mellon University) - February 2, 2023
- Computing Representations for Lie Algebraic Networks (Noah Shutty, Google) - January 26, 2023
- projUNN: efficient method for training deep networks with unitary matrices (Bobak Kiani (MIT)) - October 27, 2022
- Neural Networks with Learned Coarsening for Simplicial Complexes (Sarah McGuire (Michigan State University)) - October 13, 2022
- The Intrinsic Manifolds of Radiological Images and Their Role in Deep Learning (Nick Konz (Duke)) - October 6, 2022
- Groups and Symmetries in Statistical Models (Anna Seigal (Harvard)) - May 26, 2022
- How to choose informative representations of topological features in persistent homology (Gregory Henselman-Petrusek (PNNL)) - May 19, 2022
- Approximate vector bundles and fiberwise dimensionality reduction (Luis Scoccola (Northeastern)) - May 5, 2022
- Modeling Many-to-Many Maps (Lizzy Coda (PNNL)) - April 28, 2022
- Riemannian Geometry in Machine Learning (Isay Katsman (Cornell)) - April 21, 2022
- Towards Mechanics of Learning in Neural Networks (Daniel Kunin (Stanford) and Hidenori Tanaka (NTT Physics & Informatics Laboratories)) - April 14, 2022
- Machine Learning Beyond Accuracy (Shibani Santurkar (Stanford)) - April 7, 2022
- Functional dimension of feedforward ReLU neural networks (Kathryn Lindsey, (Boston College)) - February 10, 2022
- Applications of Group Symmetry (Emily King, (Colorado State University)) - February 3, 2022
- DNA: Dynamic Network Augmentation (Scott Mahan (University of California, San Diego)) - January 27, 2022
- A tool for deep learning model interpretability (Davis Brown (Pacific Northwest National Laboratory)) - January 20, 2022
- From Zigzags to Networks: Topological Tools for Time Series Analysis (Sarah Tymochko (Michigan State University)) - January 13, 2022
- Private AI: Machine Learning on Encrypted Data (Kristin Lauter, Facebook AI Research (FAIR)) - December 2, 2021
- TBA (Mitchell Wortsman, UW CSE) - November 18, 2021
- Chirality in Vision (Rekha Thomas, UW Math) - November 12, 2021
- Detecting Short-lasting Topics Using Nonnegative Tensor Decomposition ( Lara Kassab, Colorado State University) - November 4, 2021
- Using the linear geometry of ReLU neural networks to detect out-of-distribution inputs (Grayson Jorgenson, Pacific Northwest National Lab) - October 28, 2021
- Studying relations geometrically and topologically (Michael Robinson, American University) - October 21, 2021
- A Polynomial Time Algorithm for Constructing Equivariant Neural Networks (Marc Finzi, NYU) - October 14, 2021
- Topic Models, Methods, and Medicine (Jamie Haddock, Harvey Mudd College) - October 7, 2021