You need to log in to edit.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

no code implementations • 27 Jul 2022 • Chrysoula Kosma, Giannis Nikolentzos, Nancy Xu, Michalis Vazirgiannis

Recently neural network architectures have been widely applied to the problem of time series forecasting.

no code implementations • 27 May 2022 • Nancy Xu, Giannis Nikolentzos, Michalis Vazirgiannis, Henrik Boström

Image matching is a key component of many tasks in computer vision and its main objective is to find correspondences between features extracted from different natural images.

no code implementations • 1 Dec 2021 • Hadi Abdine, Yanzhu Guo, Moussa Kamal Eddine, Giannis Nikolentzos, Stamatis Outsios, Guokan Shang, Christos Xypolopoulos, Michalis Vazirgiannis

DaSciM (Data Science and Mining) part of LIX at Ecole Polytechnique, established in 2013 and since then producing research results in the area of large scale data analysis via methods of machine and deep learning.

1 code implementation • 5 Oct 2021 • Giannis Nikolentzos, George Dasoulas, Michalis Vazirgiannis

In this paper, we propose a new graph neural network model, so-called $\pi$-GNN which learns a "soft" permutation (i. e., doubly stochastic) matrix for each graph, and thus projects all graphs into a common vector space.

no code implementations • 29 Sep 2021 • Giannis Nikolentzos, Michalis Vazirgiannis

The proposed model retains the transparency of Random Walk Graph Neural Networks since its first layer also consists of a number of trainable ``hidden graphs'' which are compared against the input graphs using the geometric random walk kernel.

no code implementations • 17 Feb 2021 • George Dasoulas, Giannis Nikolentzos, Kevin Scaman, Aladin Virmaux, Michalis Vazirgiannis

Machine learning on graph-structured data has attracted high research interest due to the emergence of Graph Neural Networks (GNNs).

no code implementations • 1 Jan 2021 • Giannis Nikolentzos, George Panagopoulos, Michalis Vazirgiannis

Graph neural networks and graph kernels have achieved great success in solving machine learning problems on graphs.

no code implementations • NeurIPS 2020 • Giannis Nikolentzos, Michalis Vazirgiannis

The first layer of the model consists of a number of trainable ``hidden graphs'' which are compared against the input graphs using a random walk kernel to produce graph representations.

4 code implementations • 10 Sep 2020 • George Panagopoulos, Giannis Nikolentzos, Michalis Vazirgiannis

Furthermore, to account for the limited amount of training data, we capitalize on the pandemic's asynchronous outbreaks across countries and use a model-agnostic meta-learning based method to transfer knowledge from one country's model to another's.

no code implementations • 2 Mar 2020 • Changmin Wu, Giannis Nikolentzos, Michalis Vazirgiannis

Then, we employ a generative model which predicts the topology of the graph at the next time step and constructs a graph instance that corresponds to that topology.

no code implementations • 1 Mar 2020 • George Dasoulas, Giannis Nikolentzos, Kevin Scaman, Aladin Virmaux, Michalis Vazirgiannis

Moreover, on graph classification tasks, we suggest the utilization of the generated structural embeddings for the transformation of an attributed graph structure into a set of augmented node attributes.

2 code implementations • 17 Aug 2019 • Giannis Nikolentzos, Antoine J. -P. Tixier, Michalis Vazirgiannis

In this paper, we represent documents as word co-occurrence networks and propose an application of the message passing framework to NLP, the Message Passing Attention network for Document understanding (MPAD).

Ranked #1 on Document Classification on MPQA

1 code implementation • 13 Jul 2019 • Giannis Nikolentzos, George Dasoulas, Michalis Vazirgiannis

We show that the proposed architecture can identify fundamental graph properties.

no code implementations • 27 Apr 2019 • Giannis Nikolentzos, Giannis Siglidis, Michalis Vazirgiannis

Graph kernels have attracted a lot of attention during the last decade, and have evolved into a rapidly developing branch of learning on structured data.

Ranked #9 on Graph Classification on NCI1

1 code implementation • 3 Apr 2019 • Konstantinos Skianis, Giannis Nikolentzos, Stratis Limnios, Michalis Vazirgiannis

In several domains, data objects can be decomposed into sets of simpler objects.

Ranked #1 on Document Classification on Twitter

1 code implementation • 7 Aug 2018 • Giannis Nikolentzos, Michalis Vazirgiannis

The first component is a kernel between vertices, while the second component is a kernel between graphs.

1 code implementation • 6 Jun 2018 • Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios, Christos Giatsidis, Konstantinos Skianis, Michalis Vazirgiannis

The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines.

no code implementations • ICLR 2018 • Giannis Nikolentzos, Polykarpos Meladianos, Antoine J-P Tixier, Konstantinos Skianis, Michalis Vazirgiannis

Graph kernels have been successfully applied to many graph classification problems.

1 code implementation • 29 Oct 2017 • Giannis Nikolentzos, Polykarpos Meladianos, Antoine Jean-Pierre Tixier, Konstantinos Skianis, Michalis Vazirgiannis

Graph kernels have been successfully applied to many graph classification problems.

no code implementations • EMNLP 2017 • Giannis Nikolentzos, Polykarpos Meladianos, Fran{\c{c}}ois Rousseau, Yannis Stavrakas, Michalis Vazirgiannis

In this paper, we present a novel document similarity measure based on the definition of a graph kernel between pairs of documents.

no code implementations • ICLR 2018 • Antoine Jean-Pierre Tixier, Giannis Nikolentzos, Polykarpos Meladianos, Michalis Vazirgiannis

Graph learning is currently dominated by graph kernels, which, while powerful, suffer some significant limitations.

Ranked #3 on Graph Classification on RE-M12K

no code implementations • EACL 2017 • Giannis Nikolentzos, Polykarpos Meladianos, Fran{\c{c}}ois Rousseau, Yannis Stavrakas, Michalis Vazirgiannis

Recently, there has been a lot of activity in learning distributed representations of words in vector spaces.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.