Dominique Beaini

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 34

Top Topics

Convolutional Neural Networks

Graph Representation

Top Venues

Publications

Dominique Beaini, Shenyang Huang, Joao Alex Cunha, Zhiyi Li, Gabriela Moisescu-Pareja, Oleksandr Dymov, Samuel Maddrell-Mander, Callum McLean, Frederik Wenkel, Luis Müller, Jama Hussein Mohamud, Ali Parviz, Michael Craig, Michal Koziarski, Jiarui Lu, Zhaocheng Zhu, Cristian Gabellini, Kerstin Klaser, Josef Dean, Cas Wognum, Maciej Sypetkowski, Guillaume Rabusseau, Reihaneh Rabbany, Jian Tang, Christopher Morris, Mirco Ravanelli, Guy Wolf, Prudencio Tossou, Hadrien Mary, Therence Bois, Andrew W. Fitzgibbon, Blazej Banaszewski, Chad Martin, Dominic Masters
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets. ICLR (2024)
Maciej Sypetkowski, Frederik Wenkel, Farimah Poursafaei, Nia Dickson, Karush Suri, Philip Fradkin, Dominique Beaini
On the Scalability of GNNs for Molecular Graphs. CoRR (2024)
Oren Kraus, Kian Kenyon-Dean, Saber Saberian, Maryam Fallah, Peter McLean, Jess Leung, Vasudev Sharma, Ayla Khan, Jia Balakrishnan, Safiye Celik, Dominique Beaini, Maciej Sypetkowski, Chi Vicky Cheng, Kristen Morse, Maureen Makes, Ben Mabey, Berton Earnshaw
Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology. CoRR (2024)
Alexander Mathiasen, Hatem Helal, Kerstin Klaser, Paul Balanca, Josef Dean, Carlo Luschi, Dominique Beaini, Andrew W. Fitzgibbon, Dominic Masters
. CoRR (2023)
Dominic Masters, Josef Dean, Kerstin Kläser, Zhiyi Li, Samuel Maddrell-Mander, Adam Sanders, Hatem Helal, Deniz Beker, Andrew W. Fitzgibbon, Shenyang Huang, Ladislav Rampásek, Dominique Beaini
GPS++: Reviving the Art of Message Passing for Molecular Property Prediction. Trans. Mach. Learn. Res. 2023 (2023)
Alexander Mathiasen, Hatem Helal, Kerstin Klaser, Paul Balanca, Josef Dean, Carlo Luschi, Dominique Beaini, Andrew W. Fitzgibbon, Dominic Masters
. NeurIPS (2023)
Soledad Villar, Benjamin Paul Chamberlain, Yuanqi Du, Hannes Stärk, Chaitanya K. Joshi, Andreea Deac, Iulia Duta, Joshua Robinson, Yanqiao Zhu, Kexin Huang, Michelle M. Li, Sofia Bourhim, Ilia Igashov, Alexandre Duval, Mathieu Alain, Dominique Beaini, Xinyu Yuan
The Second Learning on Graphs Conference: Preface. LoG (2023)
Nikhil Shenoy, Prudencio Tossou, Emmanuel Noutahi, Hadrien Mary, Dominique Beaini, Jiarui Ding
Role of Structural and Conformational Diversity for Machine Learning Potentials. CoRR (2023)
Dominic Masters, Josef Dean, Kerstin Klaser, Zhiyi Li, Sam Maddrell-Mander, Adam Sanders, Hatem Helal, Deniz Beker, Andrew W. Fitzgibbon, Shenyang Huang, Ladislav Rampásek, Dominique Beaini
GPS++: Reviving the Art of Message Passing for Molecular Property Prediction. CoRR (2023)
Renming Liu, Semih Cantürk, Olivier Lapointe-Gagné, Vincent Létourneau, Guy Wolf, Dominique Beaini, Ladislav Rampásek
Graph Positional and Structural Encoder. CoRR (2023)
Dominique Beaini, Shenyang Huang, Joao Alex Cunha, Zhiyi Li, Gabriela Moisescu-Pareja, Oleksandr Dymov, Samuel Maddrell-Mander, Callum McLean, Frederik Wenkel, Luis Müller, Jama Hussein Mohamud, Ali Parviz, Michael Craig, Michal Koziarski, Jiarui Lu, Zhaocheng Zhu, Cristian Gabellini, Kerstin Klaser, Josef Dean, Cas Wognum, Maciej Sypetkowski, Guillaume Rabusseau, Reihaneh Rabbany, Jian Tang, Christopher Morris, Ioannis Koutis, Mirco Ravanelli, Guy Wolf, Prudencio Tossou, Hadrien Mary, Therence Bois, Andrew W. Fitzgibbon, Blazej Banaszewski, Chad Martin, Dominic Masters
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets. CoRR (2023)
Xiangyu Zhao, Hannes Stärk, Dominique Beaini, Pietro Liò, Yiren Zhao
Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration. CoRR (2023)
Vijay Prakash Dwivedi, Ladislav Rampásek, Mikhail Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, Dominique Beaini
Long Range Graph Benchmark. CoRR (2022)
Dominic Masters, Josef Dean, Kerstin Klaser, Zhiyi Li, Sam Maddrell-Mander, Adam Sanders, Hatem Helal, Deniz Beker, Ladislav Rampásek, Dominique Beaini
GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction. CoRR (2022)
Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Lió
3D Infomax improves GNNs for Molecular Property Prediction. ICML (2022)
Vijay Prakash Dwivedi, Ladislav Rampásek, Michael Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, Dominique Beaini
Long Range Graph Benchmark. NeurIPS (2022)
Ladislav Rampásek, Michael Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, Dominique Beaini
Recipe for a General, Powerful, Scalable Graph Transformer. NeurIPS (2022)
Ladislav Rampásek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, Dominique Beaini
Recipe for a General, Powerful, Scalable Graph Transformer. CoRR (2022)
Devin Kreuzer, Dominique Beaini, William L. Hamilton, Vincent Létourneau, Prudencio Tossou
Rethinking Graph Transformers with Spectral Attention. NeurIPS (2021)
Devin Kreuzer, Dominique Beaini, William L. Hamilton, Vincent Létourneau, Prudencio Tossou
Rethinking Graph Transformers with Spectral Attention. CoRR (2021)
Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Liò
3D Infomax improves GNNs for Molecular Property Prediction. CoRR (2021)
Dominique Beaini, Saro Passaro, Vincent Létourneau, William L. Hamilton, Gabriele Corso, Pietro Lió
Directional Graph Networks. ICML (2021)
Dominique Beaini, Sofiane Achiche, Alexandre Duperré, Maxime Raison
Deep green function convolution for improving saliency in convolutional neural networks. Vis. Comput. 37 (2) (2021)
Dominique Beaini, Sofiane Achiche, Maxime Raison
Improving Convolutional Neural Networks Via Conservative Field Regularisation and Integration. CoRR (2020)
Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, Petar Velickovic
Principal Neighbourhood Aggregation for Graph Nets. CoRR (2020)
Dominique Beaini, Saro Passaro, Vincent Létourneau, William L. Hamilton, Gabriele Corso, Pietro Liò
Directional Graph Networks. CoRR (2020)
Dominique Beaini, Sofiane Achiche, Alexandre Duperré, Maxime Raison
Saliency Enhancement using Gradient Domain Edges Merging. CoRR (2020)
Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, Petar Velickovic
Principal Neighbourhood Aggregation for Graph Nets. NeurIPS (2020)
Dominique Beaini, Sofiane Achiche, Alexandre Duperré, Maxime Raison
Deep Green Function Convolution for Improving Saliency in Convolutional Neural Networks. CoRR (2019)
Dominique Beaini, Sofiane Achiche, Fabrice Nonez, Olivier Brochu Dufour, Cédric Leblond-Ménard, Mahdis Asaadi, Maxime Raison
Fast and Optimal Laplacian Solver for Gradient-Domain Image Editing using Green Function Convolution. CoRR (2019)
Emmanuel Noutahi, Dominique Beaini, Julien Horwood, Prudencio Tossou
Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling. CoRR (2019)
Dominique Beaini, Sofiane Achiche, Fabrice Nonez, Maxime Raison
Computing the Spatial Probability of Inclusion inside Partial Contours for Computer Vision Applications. CoRR (2018)
Dominique Beaini, Sofiane Achiche, Yann-Seing Law-Kam Cio, Maxime Raison
Novel Convolution Kernels for Computer Vision and Shape Analysis based on Electromagnetism. CoRR (2018)
C. Bousquet-Jette, Sofiane Achiche, Dominique Beaini, Y. S. Law-Kam Cio, Cédric Leblond-Ménard, Maxime Raison
Fast scene analysis using vision and artificial intelligence for object prehension by an assistive robot. Eng. Appl. Artif. Intell. 63 (2017)