Recently neural network architectures have been widely applied to the problem of time series forecasting.
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.
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.
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.
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.
Machine learning on graph-structured data has attracted high research interest due to the emergence of Graph Neural Networks (GNNs).
Graph neural networks and graph kernels have achieved great success in solving machine learning problems on graphs.
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.
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.
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.
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.
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
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
In several domains, data objects can be decomposed into sets of simpler objects.
Ranked #1 on Document Classification on Twitter
The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines.
Graph kernels have been successfully applied to many graph classification problems.
In this paper, we present a novel document similarity measure based on the definition of a graph kernel between pairs of documents.
Graph learning is currently dominated by graph kernels, which, while powerful, suffer some significant limitations.
Ranked #3 on Graph Classification on RE-M12K
Recently, there has been a lot of activity in learning distributed representations of words in vector spaces.