Dominique Beaini

According to our database, Dominique Beaini authored at least 28 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

OpenQDC: Open Quantum Data Commons.

Cristian Gabellini, Nikhil Shenoy, Stephan Thaler, Semih Cantürk, Daniel McNeela, Dominique Beaini, Michael Bronstein, Prudencio Tossou

CoRR, 2024

ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation.

Majdi Hassan, Nikhil Shenoy, Jungyoon Lee, Hannes Stärk, Stephan Thaler, Dominique Beaini

CoRR, 2024

How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval.

Philip Fradkin, Puria Azadi, Karush Suri, Frederik Wenkel, Ali Bashashati, Maciej Sypetkowski, Dominique Beaini

CoRR, 2024

On the Scalability of GNNs for Molecular Graphs.

Maciej Sypetkowski, Frederik Wenkel, Farimah Poursafaei, Nia Dickson, Karush Suri, Philip Fradkin, Dominique Beaini

CoRR, 2024

Graph Positional and Structural Encoder.

Semih Cantürk, Renming Liu, Olivier Lapointe-Gagné, Vincent Létourneau, Guy Wolf, Dominique Beaini, Ladislav Rampásek

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets.

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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology.

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

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

GPS++: Reviving the Art of Message Passing for Molecular Property Prediction.

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

Trans. Mach. Learn. Res., 2023

Role of Structural and Conformational Diversity for Machine Learning Potentials.

Nikhil Shenoy, Prudencio Tossou, Emmanuel Noutahi, Hadrien Mary, Dominique Beaini, Jiarui Ding

CoRR, 2023

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets.

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

CoRR, 2023

Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration.

Xiangyu Zhao, Hannes Stärk, Dominique Beaini, Pietro Liò, Yiren Zhao

CoRR, 2023

Generating QM1B with PySCF_IPU.

Alexander Mathiasen, Hatem Helal, Kerstin Klaser, Paul Balanca, Josef Dean, Carlo Luschi, Dominique Beaini, Andrew W. Fitzgibbon, Dominic Masters

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

The Second Learning on Graphs Conference: Preface.

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

Proceedings of the Learning on Graphs Conference, 27-30 November 2023, Virtual Event., 2023

2022

GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction.

Dominic Masters, Josef Dean, Kerstin Klaser, Zhiyi Li, Sam Maddrell-Mander, Adam Sanders, Hatem Helal, Deniz Beker, Ladislav Rampásek, Dominique Beaini

CoRR, 2022

Recipe for a General, Powerful, Scalable Graph Transformer.

Ladislav Rampásek, Michael Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, Dominique Beaini

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Long Range Graph Benchmark.

Vijay Prakash Dwivedi, Ladislav Rampásek, Michael Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, Dominique Beaini

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

3D Infomax improves GNNs for Molecular Property Prediction.

Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Lió

Proceedings of the International Conference on Machine Learning, 2022

2021

Deep green function convolution for improving saliency in convolutional neural networks.

Dominique Beaini, Sofiane Achiche, Alexandre Duperré, Maxime Raison

Vis. Comput., 2021

Rethinking Graph Transformers with Spectral Attention.

Devin Kreuzer, Dominique Beaini, William L. Hamilton, Vincent Létourneau, Prudencio Tossou

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Directional Graph Networks.

Dominique Beaini, Saro Passaro, Vincent Létourneau, William L. Hamilton, Gabriele Corso, Pietro Lió

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Improving Convolutional Neural Networks Via Conservative Field Regularisation and Integration.

Dominique Beaini, Sofiane Achiche, Maxime Raison

CoRR, 2020

Saliency Enhancement using Gradient Domain Edges Merging.

Dominique Beaini, Sofiane Achiche, Alexandre Duperré, Maxime Raison

CoRR, 2020

Principal Neighbourhood Aggregation for Graph Nets.

Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, Petar Velickovic

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling.

Emmanuel Noutahi, Dominique Beaini, Julien Horwood, Prudencio Tossou

CoRR, 2019

Fast and Optimal Laplacian Solver for Gradient-Domain Image Editing using Green Function Convolution.

Dominique Beaini, Sofiane Achiche, Fabrice Nonez, Olivier Brochu Dufour, Cédric Leblond-Ménard, Mahdis Asaadi, Maxime Raison

CoRR, 2019

2018

Novel Convolution Kernels for Computer Vision and Shape Analysis based on Electromagnetism.

Dominique Beaini, Sofiane Achiche, Yann-Seing Law-Kam Cio, Maxime Raison

CoRR, 2018

Computing the Spatial Probability of Inclusion inside Partial Contours for Computer Vision Applications.

Dominique Beaini, Sofiane Achiche, Fabrice Nonez, Maxime Raison

CoRR, 2018

2017

Fast scene analysis using vision and artificial intelligence for object prehension by an assistive robot.

C. Bousquet-Jette, Sofiane Achiche, Dominique Beaini, Y. S. Law-Kam Cio, Cédric Leblond-Ménard, Maxime Raison

Eng. Appl. Artif. Intell., 2017