A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Sampling random points can reveal submanifold topology.
EuLearn creates diverse 3D topological datasets for machine learning.
Study evaluates synthetic data augmentation for small datasets, highlighting inconsistencies in traditional metrics.
Study shows topological features improve time series classification.
ARTree uses deep learning to infer tree topologies efficiently.
IVFS simplifies feature selection for high-dimensional data preservation.
In this study, a novel topology optimization approach based on conditional Wasserstein generative adversarial networks (CWGAN) is developed to replicate the conventional topology optimization algorithms in an extremely computationally inexpensive way. CWGAN consists of a generator and a discriminator, both of which are…
Paper uses diffusion models to design electric aircraft quickly.
Study shows effective resistance distance yields more accurate network barycenter than Hamming distance.
In this paper, we exploit minimal sensing information gathered from biologically inspired sensor networks to perform exploration and mapping in an unknown environment. A probabilistic motion model of mobile sensing nodes, inspired by motion characteristics of cockroaches, is utilized to extract weak encounter informati…
Combines geometry and topology for analyzing hierarchical datasets.
Topological data analysis (TDA) has emerged as one of the most promising techniques to reconstruct the unknown shapes of high-dimensional spaces from observed data samples. TDA, thus, yields key shape descriptors in the form of persistent topological features that can be used for any supervised or unsupervised learning…
This work introduces novel methods to identify and compare cycles across topological objects.
We study regularization in the context of small sample-size learning with over-parameterized neural networks. Specifically, we shift focus from architectural properties, such as norms on the network weights, to properties of the internal representations before a linear classifier. Specifically, we impose a topological …
Deep learning enhances Hamiltonian Monte Carlo for sampling gauge field configurations.
Study topological invariants of complexes for Riemannian manifolds.
Unified toolkit for comparing neural representations using SRTD and NTS.
Topology guidance controls generative model outputs by specifying topological features.
The paper explores statistical and topological properties of sliced probability divergences.
The paper studies the convergence of SAA for systemic risk measures.
Neural nets learn robust geometric data representations.
New model learns from random graph samples to estimate graph parameters.
Estimates network topologies from shared graphon models across different networks.
Machine learning models for repeated measurements are limited. Using topological data analysis (TDA), we present a classifier for repeated measurements which samples from the data space and builds a network graph based on the data topology. When applying this to two case studies, accuracy exceeds alternative models wit…
Paper tackles few-shot class-incremental learning with a neural gas network.
Topological Flow Matching: A Generative Modeling Framework for Structured Spaces
We implement methods from computational homology to obtain a topological signal of singularity formation in a selection of geometries evolved numerically by Ricci flow. Our approach, based on persistent homology, produces precise, quantitative measures describing the behavior of an entire collection of data across a di…
Data samples collected for training machine learning models are typically assumed to be independent and identically distributed (iid). Recent research has demonstrated that this assumption can be problematic as it simplifies the manifold of structured data. This has motivated different research areas such as data poiso…
PhyloVAE learns tree topologies without supervision.
Paper speeds up topological signal identification and cycle matching.
We present an algorithm for learning a latent variable generative model via generative adversarial learning where the canonical uniform noise input is replaced by samples from a graphical model. This graphical model is learned by a Boltzmann machine which learns low-dimensional feature representation of data extracted …
HLTF generates chemically valid 3D molecules with improved topology control.
Network sampling is integral to the analysis of social, information, and biological networks. Since many real-world networks are massive in size, continuously evolving, and/or distributed in nature, the network structure is often sampled in order to facilitate study. For these reasons, a more thorough and complete unde…
Generative Topological Networks (GTNs) simplify latent space generation.
New method for inferring network topology from partial data.
With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in visualization. First, the utilization of black box models (e.g., deep neural networks) calls for advanced techniques in exploring and interpreting model behavio…
Progressive Neural Network Learning is a class of algorithms that incrementally construct the network's topology and optimize its parameters based on the training data. While this approach exempts the users from the manual task of designing and validating multiple network topologies, it often requires an enormous numbe…
We propose two related unsupervised clustering algorithms which, for input, take data assumed to be sampled from a uniform distribution supported on a metric space , and output a clustering of the data based on the selection of a topological model for the connected components of . Both algorithms work by selectin…
A new model for graph sampling that preserves structure without explicit targeting.
Bayesian method infers network topology and dynamics from noisy, sparse measurements.
Improved phylogenetic inference using VBPI-Mixtures for tree topology and branch length.
New method detects uncertainty in neural networks for out-of-distribution detection.
We present a new family of zero-field Ising models over binary variables/spins obtained by consecutive "gluing" of planar and -sized components and subsets of at most three vertices into a tree. The polynomial-time algorithm of the dynamic programming type for solving exact inference (computing partition func…
Outlier, or anomaly, detection is essential for optimal performance of machine learning methods and statistical predictive models. It is not just a technical step in a data cleaning process but a key topic in many fields such as fraudulent document detection, in medical applications and assisted diagnosis systems or de…
The topological information is essential for studying the relationship between nodes in a network. Recently, Network Representation Learning (NRL), which projects a network into a low-dimensional vector space, has been shown their advantages in analyzing large-scale networks. However, most existing NRL methods are desi…
In the aftermath of the financial crisis, the growing literature on financial networks has widely documented the predictive power of topological characteristics (e.g. degree centrality measures) to explain the systemic impact or systemic vulnerability of financial institutions. In this work, we show that considering al…
SOM-VQ tokenizes discrete models with semantic structure and navigable topology.
Recent studies classify the topology of proteins by analysing the distribution of their projections using knotoids. The approximation of this distribution depends on the number of projection directions that are sampled. Here we investigate the relation between knotoids differing only by small perturbations of the direc…