Abstract. Viewing transformers with fixed weights as interacting particle systems, the particles, representing tokens,
tend to cluster toward particular limiting objects as time tends to infinity. With techniques from dynamical systems and PDEs,
it can be shown that the type of limiting object depends on the spectrum of the value matrix.
Abstract. Last semester there was a fair amount of coverage of quantized tensor trains (QTTs) both in this
seminar and the Applied Math Seminar. I will tell you what I've
learned meanwhile about QTTs and share some thoughts and questions regarding their future in numerical analysis..
Abstract. Review of this paper and this follow-up.
Abstract. Review of this paper.
Abstract. Low-rank approximation of symmetric positive semidefinite matrices based on column subset
selection can enable efficient algorithms for matrix sketching, experimental design, and reduced-order modeling.
Determinantal point processes (DPPs) yield theoretically rigorous bounds for the worst-case optimal performance of
such approximations. Proofs of relevant bounds have a rich (and long) history, relating to such topics as elementary
symmetric polynomials, real algebraic geometry, Schur convexity, and random matrix theory. Besides the theory of
its worst-case performance, DPP sampling has also been the subject of numerous algorithmic implementations in recent
years. In this seminar, I will give a brief introduction to some applications, underlying theory, and algorithms
related to DPPs in low-rank approximation.
Organizer: Michael Lindsey
HDSC Seminar, Spring 2024
Meeting details: Thursday 1-2, Evans 1015
Description
Welcome to an informal seminar on high-dimensional scientific computing (HDSC). We will investigate paradigms for HDSC
including tensor networks, Monte Carlo methods, semidefinite programming relaxations, graphical models, neural networks, and more, as well as tools from numerical
linear algebra and optimization.
Past semesters: [Fall 2023]
Schedule
Click for abstracts.
February 1
Speaker: Yuhang Cai [home page]
Topic: Clustering in self-attention dynamics
February 8
Speaker: Michael Lindsey [home page]
Topic: What I learned about QTTs last semester
February 15
Speaker: Michael Kielstra [home page]
Topic: Approximation by exponential sums
February 22
Speaker: Kevin Stubbs [home page]
Topic: Gauging tensor networks with belief propagation
February 29
Speaker: Mark Fornace [home page]
Topic: Determinantal point processes in low-rank approximation
Selected references:
Derezinski, MichaĆ, and Michael W. Mahoney. "Determinantal point processes in randomized numerical linear algebra." Notices of the American Mathematical Society 68.1 (2021): 34-45. [link]
Guruswami, Venkatesan, and Ali Kemal Sinop. "Optimal column-based low-rank matrix reconstruction." Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, 2012. [link]
Sample topics to present
In no particular order.
Machine learning
(See this crash course for background.)
[1 dimension] [general case]
Matrix sketching
Group synchronization
Tensor networks
Sampling with sign problems
Potpourri