1 code implementation • 30 Sep 2022 • Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca, Thomas Markovich, Nils Hammerla, Michael M. Bronstein, Max Hansmire
Our experiments show that BUDDY also outperforms SGNNs on standard LP benchmarks while being highly scalable and faster than ELPH.
1 code implementation • 9 Feb 2022 • Cristian Bodnar, Francesco Di Giovanni, Benjamin Paul Chamberlain, Pietro Liò, Michael M. Bronstein
In this paper, we use cellular sheaf theory to show that the underlying geometry of the graph is deeply linked with the performance of GNNs in heterophilic settings and their oversmoothing behaviour.
2 code implementations • ICLR 2022 • Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein
Most graph neural networks (GNNs) use the message passing paradigm, in which node features are propagated on the input graph.
Ranked #43 on Node Classification on Citeseer
1 code implementation • 23 Nov 2021 • Emanuele Rossi, Henry Kenlay, Maria I. Gorinova, Benjamin Paul Chamberlain, Xiaowen Dong, Michael Bronstein
While Graph Neural Networks (GNNs) have recently become the de facto standard for modeling relational data, they impose a strong assumption on the availability of the node or edge features of the graph.
1 code implementation • NeurIPS 2021 • Benjamin Paul Chamberlain, James Rowbottom, Davide Eynard, Francesco Di Giovanni, Xiaowen Dong, Michael M Bronstein
We propose a novel class of graph neural networks based on the discretised Beltrami flow, a non-Euclidean diffusion PDE.
1 code implementation • NeurIPS Workshop DLDE 2021 • Benjamin Paul Chamberlain, James Rowbottom, Maria Gorinova, Stefan Webb, Emanuele Rossi, Michael M. Bronstein
We present Graph Neural Diffusion (GRAND) that approaches deep learning on graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as discretisations of an underlying PDE.
1 code implementation • 18 Mar 2019 • Elaine M. Bettaney, Stephen R. Hardwick, Odysseas Zisimopoulos, Benjamin Paul Chamberlain
Combining items of clothing into an outfit is a major task in fashion retail.
no code implementations • 22 Feb 2019 • Benjamin Paul Chamberlain, Stephen R. Hardwick, David R. Wardrope, Fabon Dzogang, Fabio Daolio, Saúl Vargas
We present a large scale hyperbolic recommender system.
1 code implementation • 11 Jul 2018 • Georg L. Grob, Ângelo Cardoso, C. H. Bryan Liu, Duncan A. Little, Benjamin Paul Chamberlain
We develop a novel RNN survival model that removes the limitations of the state of the art methods.
1 code implementation • 11 Apr 2018 • Nikolaos Aletras, Benjamin Paul Chamberlain
Inferring socioeconomic attributes of social media users such as occupation and income is an important problem in computational social science.
no code implementations • ICLR 2018 • Benjamin Paul Chamberlain, James R. Clough, Marc Peter Deisenroth
Neural embeddings have been used with great success in Natural Language Processing (NLP) where they provide compact representations that encapsulate word similarity and attain state-of-the-art performance in a range of linguistic tasks.
1 code implementation • 29 Jun 2017 • C. H. Bryan Liu, Benjamin Paul Chamberlain, Duncan A. Little, Angelo Cardoso
We argue that error reduction is only one of several metrics that must be considered when optimizing random forest parameters for commercial applications.
no code implementations • 29 May 2017 • Benjamin Paul Chamberlain, James Clough, Marc Peter Deisenroth
Neural embeddings have been used with great success in Natural Language Processing (NLP).
no code implementations • 7 Mar 2017 • Benjamin Paul Chamberlain, Angelo Cardoso, C. H. Bryan Liu, Roberto Pagliari, Marc Peter Deisenroth
We detail the system deployed at ASOS and show that learning feature representations is a promising extension to the state of the art in CLTV modelling.
no code implementations • 18 Jan 2016 • Benjamin Paul Chamberlain, Clive Humby, Marc Peter Deisenroth
Enhancing Twitter data with user ages would advance our ability to study social network structures, information flows and the spread of contagions.
1 code implementation • 15 Jan 2016 • Benjamin Paul Chamberlain, Josh Levy-Kramer, Clive Humby, Marc Peter Deisenroth
For a broad range of research, governmental and commercial applications it is important to understand the allegiances, communities and structure of key players in society.