Zobrazeno 1 - 10
of 403
pro vyhledávání: '"GROHE, MARTIN"'
Autor:
Grohe, Martin
We give an overview of different approaches to measuring the similarity of, or the distance between, two graphs, highlighting connections between these approaches. We also discuss the complexity of computing the distances.
Externí odkaz:
http://arxiv.org/abs/2411.10182
Autor:
Pirnay, Jonathan, Rittig, Jan G., Wolf, Alexander B., Grohe, Martin, Burger, Jakob, Mitsos, Alexander, Grimm, Dominik G.
Generative deep learning has become pivotal in molecular design for drug discovery and materials science. A widely used paradigm is to pretrain neural networks on string representations of molecules and fine-tune them using reinforcement learning on
Externí odkaz:
http://arxiv.org/abs/2411.01667
We lay the foundations for a database-inspired approach to interpreting and understanding neural network models by querying them using declarative languages. Towards this end we study different query languages, based on first-order logic, that mainly
Externí odkaz:
http://arxiv.org/abs/2408.10362
Publikováno v:
The Twelfth International Conference on Learning Representations (2024)
Graph Transformers (GTs) such as SAN and GPS are graph processing models that combine Message-Passing GNNs (MPGNNs) with global Self-Attention. They were shown to be universal function approximators, with two reservations: 1. The initial node feature
Externí odkaz:
http://arxiv.org/abs/2405.11951
Autor:
Wolf, Hinrikus, Böttcher, Luis, Bouchkati, Sarra, Lutat, Philipp, Breitung, Jens, Jung, Bastian, Möllemann, Tina, Todosijević, Viktor, Schiefelbein-Lach, Jan, Pohl, Oliver, Ulbig, Andreas, Grohe, Martin
In the course of the energy transition, the expansion of generation and consumption will change, and many of these technologies, such as PV systems, electric cars and heat pumps, will influence the power flow, especially in the distribution grids. Sc
Externí odkaz:
http://arxiv.org/abs/2405.03262
Autor:
Grohe, Martin, Rosenbluth, Eran
Graph neural networks (GNN) are deep learning architectures for graphs. Essentially, a GNN is a distributed message passing algorithm, which is controlled by parameters learned from data. It operates on the vertices of a graph: in each iteration, ver
Externí odkaz:
http://arxiv.org/abs/2403.06817
Autor:
Morris, Christopher, Frasca, Fabrizio, Dym, Nadav, Maron, Haggai, Ceylan, İsmail İlkan, Levie, Ron, Lim, Derek, Bronstein, Michael, Grohe, Martin, Jegelka, Stefanie
Machine learning on graphs, especially using graph neural networks (GNNs), has seen a surge in interest due to the wide availability of graph data across a broad spectrum of disciplines, from life to social and engineering sciences. Despite their pra
Externí odkaz:
http://arxiv.org/abs/2402.02287
Publikováno v:
CIKM 2023
Machinery for data analysis often requires a numeric representation of the input. Towards that, a common practice is to embed components of structured data into a high-dimensional vector space. We study the embedding of the tuples of a relational dat
Externí odkaz:
http://arxiv.org/abs/2401.11215
We propose and study a framework for quantifying the importance of the choices of parameter values to the result of a query over a database. These parameters occur as constants in logical queries, such as conjunctive queries. In our framework, the im
Externí odkaz:
http://arxiv.org/abs/2401.04606
Autor:
Grohe, Martin, Neuen, Daniel
We prove that isomorphism of tournaments of twin width at most $k$ can be decided in time $k^{O(\log k)}n^{O(1)}$. This implies that the isomorphism problem for classes of tournaments of bounded or moderately growing twin width is in polynomial time.
Externí odkaz:
http://arxiv.org/abs/2312.02048