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pro vyhledávání: '"Morris Christopher"'
Discrete-state denoising diffusion models led to state-of-the-art performance in graph generation, especially in the molecular domain. Recently, they have been transposed to continuous time, allowing more flexibility in the reverse process and a bett
Externí odkaz:
http://arxiv.org/abs/2406.06449
Autor:
Müller, Luis, Morris, Christopher
Graph neural network architectures aligned with the $k$-dimensional Weisfeiler--Leman ($k$-WL) hierarchy offer theoretically well-understood expressive power. However, these architectures often fail to deliver state-of-the-art predictive performance
Externí odkaz:
http://arxiv.org/abs/2406.03148
Message-passing graph neural networks (MPNNs) have emerged as a powerful paradigm for graph-based machine learning. Despite their effectiveness, MPNNs face challenges such as under-reaching and over-squashing, where limited receptive fields and struc
Externí odkaz:
http://arxiv.org/abs/2405.17311
The Weisfeiler-Leman algorithm ($1$-WL) is a well-studied heuristic for the graph isomorphism problem. Recently, the algorithm has played a prominent role in understanding the expressive power of message-passing graph neural networks (MPNNs) and bein
Externí odkaz:
http://arxiv.org/abs/2402.07568
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
Graph learning architectures based on the k-dimensional Weisfeiler-Leman (k-WL) hierarchy offer a theoretically well-understood expressive power. However, such architectures often fail to deliver solid predictive performance on real-world tasks, limi
Externí odkaz:
http://arxiv.org/abs/2401.10119
Recently, machine learning, particularly message-passing graph neural networks (MPNNs), has gained traction in enhancing exact optimization algorithms. For example, MPNNs speed up solving mixed-integer optimization problems by imitating computational
Externí odkaz:
http://arxiv.org/abs/2310.10603
Autor:
Beaini, Dominique, Huang, Shenyang, Cunha, Joao Alex, Li, Zhiyi, Moisescu-Pareja, Gabriela, Dymov, Oleksandr, Maddrell-Mander, Samuel, McLean, Callum, Wenkel, Frederik, Müller, Luis, Mohamud, Jama Hussein, Parviz, Ali, Craig, Michael, Koziarski, Michał, Lu, Jiarui, Zhu, Zhaocheng, Gabellini, Cristian, Klaser, Kerstin, Dean, Josef, Wognum, Cas, Sypetkowski, Maciej, Rabusseau, Guillaume, Rabbany, Reihaneh, Tang, Jian, Morris, Christopher, Koutis, Ioannis, Ravanelli, Mirco, Wolf, Guy, Tossou, Prudencio, Mary, Hadrien, Bois, Therence, Fitzgibbon, Andrew, Banaszewski, Błażej, Martin, Chad, Masters, Dominic
Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, where datasets are often hand-curated, and hence typically small, the lack of datasets with labeled features, an
Externí odkaz:
http://arxiv.org/abs/2310.04292
Autor:
Qian, Chendi, Manolache, Andrei, Ahmed, Kareem, Zeng, Zhe, Broeck, Guy Van den, Niepert, Mathias, Morris, Christopher
Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structured input. However, they operate on a fixed input graph structure, ignoring potential noise and missing information. Furthermore, their local aggregat
Externí odkaz:
http://arxiv.org/abs/2310.02156