Fetching the paper…
Reading the bibliography…
Topological Neural Networks (TNNs) incorporate higher-order relational information beyond pairwise interactions, enabling richer representations than Graph Neural Networks (GNNs).
The reduction of a graph to canonical form and the algebra which appears therein
Boris Weisfeiler and Andrei Leman · 1968
Earlier work this paper cites.
A solution for the best rotation to relate two sets of vectors
Wolfgang Kabsch · 1976
Earlier work this paper cites.
Directional message passing for molecular graphs
Johannes Gasteiger, Janek Groß, and Stephan Günnemann · 2003
Earlier work this paper cites.
The development of social network analysis
Linton Freeman · 2004
Earlier work this paper cites.
Analyse numérique et équations différentielles
Jean-Pierre Demailly · 2006
Earlier work this paper cites.
Computational Topology - an Introduction
H. Edelsbrunner and J. Harer · 2010
Earlier work this paper cites.
Fast and uncertainty-aware directional message passing for non-equilibrium molecules
Johannes Gasteiger, Shankari Giri, Johannes T Margraf, and Stephan Günnemann · 2011
Earlier work this paper cites.
Sabdab: the structural antibody database
James Dunbar, Konrad Krawczyk, Jinwoo Leem, Terry Baker, Angelika Fuchs, Guy Georges, Jiye Shi, and Charlotte M Deane · 2014
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
Earlier work this paper cites.
Statistical topological data analysis using persistence landscapes
P. Bubenik · 2015
Earlier work this paper cites.
Persistence Images: A Stable Vector Representation of Persistent Homology
Henry Adams, Tegan Emerson, Michael Kirby, Rachel Neville, Chris Peterson, Patrick Shipman, Sofya Chepushtanova, Eric Hanson, Francis Motta, and Lori Ziegelmeier · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
Persistence weighted Gaussian kernel for topological data analysis
Genki Kusano, Yasuaki Hiraoka, and Kenji Fukumizu · 2016
Earlier work this paper cites.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
Earlier work this paper cites.
Deep learning with topological signatures
C. Hofer, R. Kwitt, M. Niethammer, and A. Uhl · 2017
Earlier work this paper cites.
Petar Velicković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
Earlier work this paper cites.
A proposal on machine learning via dynamical systems
Ee Weinan · 2017
Earlier work this paper cites.
Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
Earlier work this paper cites.
Neural ordinary differential equations
Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
Earlier work this paper cites.
Ffjord: Free-form continuous dynamics for scalable reversible generative models
Will Grathwohl, Ricky TQ Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud · 2018
Earlier work this paper cites.
Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations
Yiping Lu, Aoxiao Zhong, Quanzheng Li, and Bin Dong · 2018
Earlier work this paper cites.
Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
Earlier work this paper cites.
Mixed high-order attention network for person re-identification
Binghui Chen, Weihong Deng, and Jiani Hu · 2019
Earlier work this paper cites.
Augmented neural odes
Emilien Dupont, Arnaud Doucet, and Yee Whye Teh · 2019
Cited alongside, same era.
Graph neural ordinary differential equations
Michael Poli, Stefano Massaroli, Junyoung Park, Atsushi Yamashita, Hajime Asama, and Jinkyoo Park · 2019
Cited alongside, same era.
How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2019
Cited alongside, same era.
ODE2VAE: Deep generative second order ODEs with Bayesian neural networks
Cagatay Yildiz, Markus Heinonen, and Harri Lahdesmaki · 2019
Cited alongside, same era.
Topological signal processing over simplicial complexes
Sergio Barbarossa and Stefania Sardellitti · 2020
Cited alongside, same era.
PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures
e3nn: Euclidean neural networks
Mario Geiger and Tess Smidt · 2022
Later among the works it cites.
Prediction of protein–protein interaction using graph neural networks
Kanchan Jha, Sriparna Saha, and Hiteshi Singh · 2022
Later among the works it cites.
Iterative refinement graph neural network for antibody sequence-structure co-design, 2022
Wengong Jin, Jeremy Wohlwend, Regina Barzilay, and Tommi Jaakkola · 2022
Later among the works it cites.
Do residual neural networks discretize neural ordinary differential equations?
Michael Sander, Pierre Ablin, and Gabriel Peyré · 2022
Later among the works it cites.
Grand++: Graph neural diffusion with a source term
Matthew Thorpe, Tan Minh Nguyen, Heidi Xia, Thomas Strohmer, Andrea Bertozzi, Stanley Osher, and Bao Wang · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mathieu Carrière, Frédéric Chazal, Yuichi Ike, Théo Lacombe, Martin Royer, and Yuhei Umeda · 2020
Cited alongside, same era.
Hnhn: Hypergraph networks with hyperedge neurons
Yihe Dong, Will Sawin, and Yoshua Bengio · 2020
Cited alongside, same era.
Se (3)-transformers: 3d roto-translation equivariant attention networks
Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling · 2020
Cited alongside, same era.
Generalization and representational limits of graph neural networks
Vikas K. Garg, Stefanie Jegelka, and Tommi Jaakkola · 2020
Cited alongside, same era.
Learning continuous-time pdes from sparse data with graph neural networks
Valerii Iakovlev, Markus Heinonen, and Harri Lähdesmäki · 2020
Cited alongside, same era.
Masked label prediction: Unified message passing model for semi-supervised classification
Yunsheng Shi, Zhengjie Huang, Shikun Feng, Hui Zhong, Wenjin Wang, and Yu Sun · 2020
Cited alongside, same era.
Geometric deep learning: Grids, groups, graphs, geodesics, and gauges, 2021
Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Velicković · 2021
Cited alongside, same era.
Yogesh Verma, Samuel Kaski, Markus Heinonen, and Vikas Garg · 2022
Later among the works it cites.
Clifford neural layers for PDE modeling
Johannes Brandstetter, Rianne van den Berg, Max Welling, and Jayesh Gupta · 2023
Later among the works it cites.
Geometric algebra transformers
Johann Brehmer, Pim De Haan, Sönke Behrends, and Taco Cohen · 2023
Later among the works it cites.
A hitchhiker’s guide to geometric gnns for 3d atomic systems, 2023
Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D. Malliaros, Taco Cohen, Pietro Lio, Yoshua Bengio, and Michael Bronstein · 2023
Later among the works it cites.
E(n) equivariant message passing simplicial networks
Floor Eijkelboom, Rob Hesselink, and Erik Bekkers · 2023
Later among the works it cites.
Cell attention networks
Lorenzo Giusti, Claudio Battiloro, Lucia Testa, Paolo Di Lorenzo, Stefania Sardellitti, and Sergio Barbarossa · 2023
Later among the works it cites.
Going beyond persistent homology using persistent homology
Johanna Immonen, Amauri H. Souza, and Vikas Garg · 2023
Later among the works it cites.
On the expressive power of geometric graph neural networks, 2023
Chaitanya K. Joshi, Cristian Bodnar, Simon V. Mathis, Taco Cohen, and Pietro Liò · 2023
Later among the works it cites.
Trainability, expressivity and interpretability in gated neural odes
Timothy Doyeon Kim, Tankut Can, and Kamesh Krishnamurthy · 2023
Later among the works it cites.
Conditional antibody design as 3d equivariant graph translation, 2023
Xiangzhe Kong, Wenbing Huang, and Yang Liu · 2023
Later among the works it cites.
Flow matching for generative modeling, 2023
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le · 2023
Later among the works it cites.
Generalization bounds for neural ordinary differential equations and deep residual networks
Pierre Marion · 2023
Later among the works it cites.
Architectures of topological deep learning: A survey on topological neural networks
Mathilde Papillon, Sophia Sanborn, Mustafa Hajij, and Nina Miolane · 2023
Later among the works it cites.
On the expressivity of persistent homology in graph learning
B. Rieck · 2023
Later among the works it cites.
AbODE: Ab initio antibody design using conjoined ODEs
Yogesh Verma, Markus Heinonen, and Vikas Garg · 2023
Later among the works it cites.
Curvature filtrations for graph generative model evaluation
Joshua Southern, Jeremy Wayland, Michael Bronstein, and Bastian Rieck · 2024
Closest in time.
ClimODE: Climate and weather forecasting with physics-informed neural ODEs
Yogesh Verma, Markus Heinonen, and Vikas Garg · 2024
Closest in time.
E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E. Smidt, and Boris Kozinsky · 2041
Closest in time.