10 citations · 21 across the 10 of their papers we have counts for
13 papers
Persistence-Augmented Neural Networks
Elena Xinyi Wang, Arnur Nigmetov, Dmitriy Morozov
Topological Data Analysis (TDA) provides tools to describe the shape of data, but integrating topological features into deep learning pipelines remains challenging, especially when…
Joint Estimation of Properties of the Lunar Subsurface and Galactic Foregrounds with LuSEE-Night
Fatima Yousuf, Zack Li, Stuart D. Bale +36
The Lunar Surface Electromagnetics Experiment (LuSEE-Night) is a joint NASA-DOE-ESA low-frequency radio telescope that will reach the lunar far side in 2027. The unknown dielectric…
On Neural Scaling Laws for Weather Emulation through Continual Training
Shashank Subramanian, Alexander Kiefer, Arnur Nigmetov +3
Neural scaling laws, which in some domains can predict the performance of large neural networks as a function of model, data, and compute scale, are the cornerstone of building fou…
Topological potentials guiding protein self-assembly
Ivan Spirandelli, Arnur Nigmetov, Dmitriy Morozov +1
The simulated self-assembly of molecular building blocks into functional complexes is a key area of study in computational biology and materials science. Self-assembly simulations…
Distributed Computation of Persistent Cohomology
Arnur Nigmetov, Dmitriy Morozov
Persistent (co)homology is a central construction in topological data analysis, where it is used to quantify prominence of features in data to produce stable descriptors suitable f…
Wilkins: HPC In Situ Workflows Made Easy
Orcun Yildiz, Dmitriy Morozov, Arnur Nigmetov +2
In situ approaches can accelerate the pace of scientific discoveries by allowing scientists to perform data analysis at simulation time. Current in situ workflow systems, however,…
Robustifying State-space Models for Long Sequences via Approximate Diagonalization
Annan Yu, Arnur Nigmetov, Dmitriy Morozov +2
State-space models (SSMs) have recently emerged as a framework for learning long-range sequence tasks. An example is the structured state-space sequence (S4) layer, which uses the…
Fast Merge Tree Computation via SYCL
Arnur Nigmetov, Dmitriy Morozov
A merge tree is a topological descriptor of a real-valued function. Merge trees are used in visualization and topological data analysis, either directly or as a means to another en…
Topological Optimization with Big Steps
Arnur Nigmetov, Dmitriy Morozov
Using persistent homology to guide optimization has emerged as a novel application of topological data analysis. Existing methods treat persistence calculation as a black box and b…
Topological Regularization via Persistence-Sensitive Optimization
Arnur Nigmetov, Aditi S. Krishnapriyan, Nicole Sanderson +1
Optimization, a key tool in machine learning and statistics, relies on regularization to reduce overfitting. Traditional regularization methods control a norm of the solution to en…
Efficient Approximation of the Matching Distance for 2-parameter persistence
Michael Kerber, Arnur Nigmetov
The matching distance is a computationally tractable topological measure to compare multi-filtered simplicial complexes. We design efficient algorithms for approximating the matchi…
Metric Spaces with Expensive Distances
Michael Kerber, Arnur Nigmetov
In algorithms for finite metric spaces, it is common to assume that the distance between two points can be computed in constant time, and complexity bounds are expressed only in te…
Geometry Helps to Compare Persistence Diagrams
Michael Kerber, Dmitriy Morozov, Arnur Nigmetov
Exploiting geometric structure to improve the asymptotic complexity of discrete assignment problems is a well-studied subject. In contrast, the practical advantages of using geomet…