Zobrazeno 1 - 10
of 205
pro vyhledávání: '"Bresson, Xavier"'
Autor:
Liu, Nian, He, Xiaoxin, Laurent, Thomas, Di Giovanni, Francesco, Bronstein, Michael M., Bresson, Xavier
Spectral graph convolution, an important tool of data filtering on graphs, relies on two essential decisions; selecting spectral bases for signal transformation and parameterizing the kernel for frequency analysis. While recent techniques mainly focu
Externí odkaz:
http://arxiv.org/abs/2405.13806
Autor:
He, Xiaoxin, Tian, Yijun, Sun, Yifei, Chawla, Nitesh V., Laurent, Thomas, LeCun, Yann, Bresson, Xavier, Hooi, Bryan
Given a graph with textual attributes, we enable users to `chat with their graph': that is, to ask questions about the graph using a conversational interface. In response to a user's questions, our method provides textual replies and highlights the r
Externí odkaz:
http://arxiv.org/abs/2402.07630
Autor:
Zhang, Yuchen, Zhang, Tianle, Wang, Kai, Guo, Ziyao, Liang, Yuxuan, Bresson, Xavier, Jin, Wei, You, Yang
Graph condensation aims to reduce the size of a large-scale graph dataset by synthesizing a compact counterpart without sacrificing the performance of Graph Neural Networks (GNNs) trained on it, which has shed light on reducing the computational cost
Externí odkaz:
http://arxiv.org/abs/2402.05011
Graph Neural Networks (GNNs) have become the preferred tool to process graph data, with their efficacy being boosted through graph data augmentation techniques. Despite the evolution of augmentation methods, issues like graph property distortions and
Externí odkaz:
http://arxiv.org/abs/2401.09953
Transformers have recently emerged as powerful neural networks for graph learning, showcasing state-of-the-art performance on several graph property prediction tasks. However, these results have been limited to small-scale graphs, where the computati
Externí odkaz:
http://arxiv.org/abs/2312.11109
Representation learning on text-attributed graphs (TAGs) has become a critical research problem in recent years. A typical example of a TAG is a paper citation graph, where the text of each paper serves as node attributes. Initial graph neural networ
Externí odkaz:
http://arxiv.org/abs/2305.19523
We formalize and study a phenomenon called feature collapse that makes precise the intuitive idea that entities playing a similar role in a learning task receive similar representations. As feature collapse requires a notion of task, we leverage a si
Externí odkaz:
http://arxiv.org/abs/2305.16162
Graph Neural Networks (GNNs) have shown great potential in the field of graph representation learning. Standard GNNs define a local message-passing mechanism which propagates information over the whole graph domain by stacking multiple layers. This p
Externí odkaz:
http://arxiv.org/abs/2212.13350
Publikováno v:
IEEE International Conference on Image Processing (ICIP), 2021, pp. 1334-1338
With the emergence of social media, voluminous video clips are uploaded every day, and retrieving the most relevant visual content with a language query becomes critical. Most approaches aim to learn a joint embedding space for plain textual and visu
Externí odkaz:
http://arxiv.org/abs/2206.12849
A quest to determine the complete sequence of a human DNA from telomere to telomere started three decades ago and was finally completed in 2021. This accomplishment was a result of a tremendous effort of numerous experts who engineered various tools
Externí odkaz:
http://arxiv.org/abs/2206.00668