Searched for: author%3A%22Yang%2C+Maosheng%22
(1 - 10 of 10)
document
Yang, Maosheng (author), Das, B. (author), Isufi, E. (author)
Simplicial convolutional filters can process signals defined over levels of a simplicial complex such as nodes, edges, triangles, and so on with applications in e.g., flow prediction in transportation or financial networks. However, the underlying topology expands over time in a way that new edges and triangles form. For example, in a...
conference paper 2023
document
Papillon, Mathilde (author), Hajij, Mustafa (author), Myers, Audun (author), Frantzen, Florian (author), Zamzmi, Ghada (author), Jenne, Helen (author), Mathe, Johan (author), Hoppe, Josef (author), Yang, Maosheng (author)
This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competition asked participants to provide open-source implementations of topological neural networks from the literature by contributing to the python packages TopoNetX ...
journal article 2023
document
Isufi, E. (author), Yang, Maosheng (author)
This paper proposes convolutional filtering for data whose structure can be modeled by a simplicial complex (SC). SCs are mathematical tools that not only capture pairwise relationships as graphs but account also for higher-order network structures. These filters are built by following the shift-and-sum principle of the convolution operation and...
conference paper 2022
document
Yang, Maosheng (author), Isufi, E. (author)
Reconstructing simplicial signals, e.g., signals defined on nodes, edges, triangles, etc., of a network, from (partial) noisy observation is of interest in water/traffic flow estimation or currency exchange markets. Typically, this concerns solving a regularised problem w.r.t. the l2 norm of the divergence or the curl of the signal, i.e., the...
conference paper 2022
document
Yang, Maosheng (author), Isufi, E. (author), Schaub, Michael T. (author), Leus, G.J.T. (author)
We study linear filters for processing signals supported on abstract topological spaces modeled as simplicial complexes, which may be interpreted as generalizations of graphs that account for nodes, edges, triangular faces, etc. To process such signals, we develop simplicial convolutional filters defined as matrix polynomials of the lower and...
journal article 2022
document
Yang, Maosheng (author), Isufi, E. (author), Leus, G.J.T. (author)
Graphs can model networked data by representing them as nodes and their pairwise relationships as edges. Recently, signal processing and neural networks have been extended to process and learn from data on graphs, with achievements in tasks like graph signal reconstruction, graph or node classifications, and link prediction. However, these...
conference paper 2022
document
Yang, Maosheng (author), Isufi, E. (author), Schaub, Michael T. (author), Leus, G.J.T. (author)
In this paper, we study linear filters to process signals defined on simplicial complexes, i.e., signals defined on nodes, edges, triangles, etc. of a simplicial complex, thereby generalizing filtering operations for graph signals. We propose a finite impulse response filter based on the Hodge Laplacian, and demonstrate how this filter can be...
conference paper 2021
document
Yang, Maosheng (author), Coutino, Mario (author), Leus, G.J.T. (author), Isufi, E. (author)
A critical task in graph signal processing is to estimate the true signal from noisy observations over a subset of nodes, also known as the reconstruction problem. In this paper, we propose a node-adaptive regularization for graph signal reconstruction, which surmounts the conventional Tikhonov regularization, giving rise to more degrees of...
journal article 2021
document
Leus, G.J.T. (author), Yang, Maosheng (author), Coutino, Mario (author), Isufi, E. (author)
To deal with high-dimensional data, graph filters have shown their power in both graph signal processing and data science. However, graph filters process signals exploiting only pairwise interactions between the nodes, and they are not able to exploit more complicated topological structures. Graph Volterra models, on the other hand, are also...
conference paper 2021
document
Yang, Maosheng (author)
This thesis consists of two parts in both data science and signal processing over graphs. In the first part of this thesis, we aim to solve the problem of graph construction in big data scenario, which is critical for practical tasks, like collaborative filtering in recommender systems, spectral embedding or clustering in learning algorithms. We...
master thesis 2020
Searched for: author%3A%22Yang%2C+Maosheng%22
(1 - 10 of 10)