Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Kanatsoulis, Charilaos I."'
Autor:
Muthusamy, Shreyas, Owerko, Damian, Kanatsoulis, Charilaos I., Agarwal, Saurav, Ribeiro, Alejandro
Unlabeled motion planning involves assigning a set of robots to target locations while ensuring collision avoidance, aiming to minimize the total distance traveled. The problem forms an essential building block for multi-robot systems in applications
Externí odkaz:
http://arxiv.org/abs/2409.19829
Network alignment is the task of establishing one-to-one correspondences between the nodes of different graphs and finds a plethora of applications in high-impact domains. However, this task is known to be NP-hard in its general form, and existing al
Externí odkaz:
http://arxiv.org/abs/2310.03272
Autor:
Owerko, Damian, Kanatsoulis, Charilaos I., Bondarchuk, Jennifer, Bucci Jr, Donald J., Ribeiro, Alejandro
Recent advances in hardware and big data acquisition have accelerated the development of deep learning techniques. For an extended period of time, increasing the model complexity has led to performance improvements for various tasks. However, this tr
Externí odkaz:
http://arxiv.org/abs/2307.11588
Over the past decade, deep learning research has been accelerated by increasingly powerful hardware, which facilitated rapid growth in the model complexity and the amount of data ingested. This is becoming unsustainable and therefore refocusing on ef
Externí odkaz:
http://arxiv.org/abs/2306.08191
Autor:
Owerko, Damian, Kanatsoulis, Charilaos I., Bondarchuk, Jennifer, Bucci Jr, Donald J., Ribeiro, Alejandro
Multi-target tracking (MTT) is a classical signal processing task, where the goal is to estimate the states of an unknown number of moving targets from noisy sensor measurements. In this paper, we revisit MTT from a deep learning perspective and prop
Externí odkaz:
http://arxiv.org/abs/2210.15539
Despite the remarkable success of Graph Neural Networks (GNNs), the common belief is that their representation power is limited and that they are at most as expressive as the Weisfeiler-Lehman (WL) algorithm. In this paper, we argue the opposite and
Externí odkaz:
http://arxiv.org/abs/2205.09801
We introduce space-time graph neural network (ST-GNN), a novel GNN architecture, tailored to jointly process the underlying space-time topology of time-varying network data. The cornerstone of our proposed architecture is the composition of time and
Externí odkaz:
http://arxiv.org/abs/2110.02880
Node embedding is the task of extracting concise and informative representations of certain entities that are connected in a network. Various real-world networks include information about both node connectivity and certain node attributes, in the for
Externí odkaz:
http://arxiv.org/abs/2011.01422
Knowledge graphs (KGs) are powerful tools that codify relational behaviour between entities in knowledge bases. KGs can simultaneously model many different types of subject-predicate-object and higher-order relations. As such, they offer a flexible m
Externí odkaz:
http://arxiv.org/abs/2010.11367
Generalized Canonical Correlation Analysis (GCCA) is an important tool that finds numerous applications in data mining, machine learning, and artificial intelligence. It aims at finding `common' random variables that are strongly correlated across mu
Externí odkaz:
http://arxiv.org/abs/2003.11205