Zobrazeno 1 - 10
of 112
pro vyhledávání: '"Coskunuzer, Baris"'
Topological Machine Learning (TML) is an emerging field that leverages techniques from algebraic topology to analyze complex data structures in ways that traditional machine learning methods may not capture. This tutorial provides a comprehensive int
Externí odkaz:
http://arxiv.org/abs/2409.02901
Autor:
Shirzadkhani, Razieh, Ngo, Tran Gia Bao, Shamsi, Kiarash, Huang, Shenyang, Poursafaei, Farimah, Azad, Poupak, Rabbany, Reihaneh, Coskunuzer, Baris, Rabusseau, Guillaume, Akcora, Cuneyt Gurcan
The field of temporal graph learning aims to learn from evolving network data to forecast future interactions. Given a collection of observed temporal graphs, is it possible to predict the evolution of an unseen network from the same domain? To answe
Externí odkaz:
http://arxiv.org/abs/2406.10426
In this paper, we bring a new perspective to persistent homology by incorporating key concepts from metric geometry. For a given compact subset $X$ of a Banach space $Y$, we study the topological features appearing in family $N_\bullet(X\subset Y)$ o
Externí odkaz:
http://arxiv.org/abs/2403.13980
Publikováno v:
AAAI 2024
Learning time-evolving objects such as multivariate time series and dynamic networks requires the development of novel knowledge representation mechanisms and neural network architectures, which allow for capturing implicit time-dependent information
Externí odkaz:
http://arxiv.org/abs/2401.13157
Autor:
Segovia-Dominguez, Ignacio, Chen, Yuzhou, Akcora, Cuneyt G., Zhen, Zhiwei, Kantarcioglu, Murat, Gel, Yulia R., Coskunuzer, Baris
Publikováno v:
LoG 2023
Topological data analysis (TDA) is gaining prominence across a wide spectrum of machine learning tasks that spans from manifold learning to graph classification. A pivotal technique within TDA is persistent homology (PH), which furnishes an exclusive
Externí odkaz:
http://arxiv.org/abs/2401.13713
The rise of cryptocurrencies like Bitcoin, which enable transactions with a degree of pseudonymity, has led to a surge in various illicit activities, including ransomware payments and transactions on darknet markets. These illegal activities often ut
Externí odkaz:
http://arxiv.org/abs/2306.07974
Topological data analysis (TDA) delivers invaluable and complementary information on the intrinsic properties of data inaccessible to conventional methods. However, high computational costs remain the primary roadblock hindering the successful applic
Externí odkaz:
http://arxiv.org/abs/2211.13708
Autor:
Demir, Andac, Coskunuzer, Baris, Segovia-Dominguez, Ignacio, Chen, Yuzhou, Gel, Yulia, Kiziltan, Bulent
In computer-aided drug discovery (CADD), virtual screening (VS) is used for identifying the drug candidates that are most likely to bind to a molecular target in a large library of compounds. Most VS methods to date have focused on using canonical co
Externí odkaz:
http://arxiv.org/abs/2211.03808
Graph neural networks (GNNs) have emerged as a powerful tool for graph classification and representation learning. However, GNNs tend to suffer from over-smoothing problems and are vulnerable to graph perturbations. To address these challenges, we pr
Externí odkaz:
http://arxiv.org/abs/2110.15529
Autor:
Coskunuzer, Baris, Akcora, CUneyt Gurcan, Dominguez, Ignacio Segovia, Zhen, Zhiwei, Kantarcioglu, Murat, Gel, Yulia R.
The machinery of topological data analysis becomes increasingly popular in a broad range of machine learning tasks, ranging from anomaly detection and manifold learning to graph classification. Persistent homology is one of the key approaches here, a
Externí odkaz:
http://arxiv.org/abs/2104.04787