Zobrazeno 1 - 10
of 4 352
pro vyhledávání: '"A. Fountoulakis"'
Counting is a fundamental skill for various visual tasks in real-life applications, requiring both object recognition and robust counting capabilities. Despite their advanced visual perception, large vision-language models (LVLMs) struggle with count
Externí odkaz:
http://arxiv.org/abs/2412.00686
Autor:
de Luca, Artur Back, Giapitzakis, George, Yang, Shenghao, Veličković, Petar, Fountoulakis, Kimon
There has been a growing interest in the ability of neural networks to solve algorithmic tasks, such as arithmetic, summary statistics, and sorting. While state-of-the-art models like Transformers have demonstrated good generalization performance on
Externí odkaz:
http://arxiv.org/abs/2410.01686
Machine learning for node classification on graphs is a prominent area driven by applications such as recommendation systems. State-of-the-art models often use multiple graph convolutions on the data, as empirical evidence suggests they can enhance p
Externí odkaz:
http://arxiv.org/abs/2405.13987
The execution of graph algorithms using neural networks has recently attracted significant interest due to promising empirical progress. This motivates further understanding of how neural networks can replicate reasoning steps with relational data. I
Externí odkaz:
http://arxiv.org/abs/2402.01107
We study the evolution of majority dynamics with more than two states on the binomial random graph $G(n,p)$. In this process, each vertex has a state in $\{1,\ldots, k\}$, with $k\geq 3$, and at each round every vertex adopts state $i$ if it has more
Externí odkaz:
http://arxiv.org/abs/2311.09078
The growing interest in machine learning problems over graphs with additional node information such as texts, images, or labels has popularized methods that require the costly operation of processing the entire graph. Yet, little effort has been made
Externí odkaz:
http://arxiv.org/abs/2310.08031
We study the node classification problem on feature-decorated graphs in the sparse setting, i.e., when the expected degree of a node is $O(1)$ in the number of nodes, in the fixed-dimensional asymptotic regime, i.e., the dimension of the feature data
Externí odkaz:
http://arxiv.org/abs/2305.10391
We consider the minimization of the cost of actuation error under resource constraints for real-time tracking in wireless autonomous systems. A transmitter monitors the state of a discrete random process and sends updates to the receiver over an unre
Externí odkaz:
http://arxiv.org/abs/2303.04908
Autor:
Yang, Shenghao, Fountoulakis, Kimon
Local graph clustering methods aim to detect small clusters in very large graphs without the need to process the whole graph. They are fundamental and scalable tools for a wide range of tasks such as local community detection, node ranking and node e
Externí odkaz:
http://arxiv.org/abs/2301.13187
Autor:
Fountoulakis, Kimon, He, Dake, Lattanzi, Silvio, Perozzi, Bryan, Tsitsulin, Anton, Yang, Shenghao
The recent years we have seen the rise of graph neural networks for prediction tasks on graphs. One of the dominant architectures is graph attention due to its ability to make predictions using weighted edge features and not only node features. In th
Externí odkaz:
http://arxiv.org/abs/2210.10014