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)

Computing the Spatial Probability of Inclusion inside Partial Contours for Computer Vision Applications. - Dominique Beaini; Sofiane Achiche; Fabrice Nonez; Maxime Raison (CoRR, 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)

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)

Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling. - Emmanuel Noutahi; Dominique Beaini; Julien Horwood; Prudencio Tossou (CoRR, 2019)

Principal Neighbourhood Aggregation for Graph Nets. - Gabriele Corso; Luca Cavalleri; Dominique Beaini; Pietro Liò; Petar Velickovic (NeurIPS, 2020)

Saliency Enhancement using Gradient Domain Edges Merging. - Dominique Beaini; Sofiane Achiche; Alexandre Duperré; Maxime Raison (CoRR, 2020)

Improving Convolutional Neural Networks Via Conservative Field Regularisation and Integration. - Dominique Beaini; Sofiane Achiche; Maxime Raison (CoRR, 2020)

Directional Graph Networks. - Dominique Beaini; Saro Passaro; Vincent Létourneau; William L. Hamilton; Gabriele Corso; Pietro Lió (ICML, 2021)

Rethinking Graph Transformers with Spectral Attention. - Devin Kreuzer; Dominique Beaini; William L. Hamilton; Vincent Létourneau; Prudencio Tossou (NeurIPS, 2021)

Deep green function convolution for improving saliency in convolutional neural networks. - Dominique Beaini; Sofiane Achiche; Alexandre Duperré; Maxime Raison (Vis. Comput., 2021)

3D Infomax improves GNNs for Molecular Property Prediction. - Hannes Stärk; Dominique Beaini; Gabriele Corso; Prudencio Tossou; Christian Dallago; Stephan Günnemann; Pietro Lió (ICML, 2022)

Long Range Graph Benchmark. - Vijay Prakash Dwivedi; Ladislav Rampásek; Michael Galkin; Ali Parviz; Guy Wolf; Anh Tuan Luu; Dominique Beaini (NeurIPS, 2022)

Recipe for a General, Powerful, Scalable Graph Transformer. - Ladislav Rampásek; Michael Galkin; Vijay Prakash Dwivedi; Anh Tuan Luu; Guy Wolf; Dominique Beaini (NeurIPS, 2022)

GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction. - Dominic Masters; Josef Dean; Kerstin Klaser 001; Zhiyi Li; Sam Maddrell-Mander; Adam Sanders; Hatem Helal; Deniz Beker; Ladislav Rampásek; Dominique Beaini (CoRR, 2022)

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 001; Yanqiao Zhu 001; Kexin Huang; Michelle M. Li; Sofia Bourhim; et al. (LoG, 2023)

Generating QM1B with PySCFIPU. - Alexander Mathiasen; Hatem Helal; Kerstin Klaser 001; Paul Balanca; Josef Dean; Carlo Luschi; Dominique Beaini; Andrew W. Fitzgibbon; Dominic Masters (NeurIPS, 2023)

Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration. - Xiangyu Zhao; Hannes Stärk; Dominique Beaini; Pietro Liò; Yiren Zhao (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; et al. (CoRR, 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)

GPS++: Reviving the Art of Message Passing for Molecular Property Prediction. - Dominic Masters; Josef Dean; Kerstin Kläser 001; 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)

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; et al. (CVPR, 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; et al. (ICLR, 2024)

Graph Positional and Structural Encoder. - Semih Cantürk; Renming Liu; Olivier Lapointe-Gagné; Vincent Létourneau; Guy Wolf; Dominique Beaini; Ladislav Rampásek (ICML, 2024)

How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval. - Philip Fradkin; Puria Azadi Moghadam; Karush Suri; Frederik Wenkel; Ali Bashashati; Maciej Sypetkowski; Dominique Beaini (NeurIPS, 2024)

ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation. - Majdi Hassan; Nikhil Shenoy; Jungyoon Lee; Hannes Stärk; Stephan Thaler; Dominique Beaini (NeurIPS, 2024)

On the Scalability of GNNs for Molecular Graphs. - Maciej Sypetkowski; Frederik Wenkel; Farimah Poursafaei; Nia Dickson; Karush Suri; Philip Fradkin; Dominique Beaini (NeurIPS, 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)

OpenQDC: Open Quantum Data Commons. - Cristian Gabellini; Nikhil Shenoy; Stephan Thaler; Semih Cantürk; Daniel McNeela; Dominique Beaini; Michael M. Bronstein; Prudencio Tossou (CoRR, 2024)

Virtual Cells: Predict, Explain, Discover. - Emmanuel Noutahi; Jason S. Hartford; Prudencio Tossou; Shawn Whitfield; Alisandra K. Denton; Cas Wognum; Kristina Ulicna; Michael Craig; Jonathan Hsu; Michael Cuccarese; Emmanuel Bengio; Dominique Beaini; et al. (CoRR, 2025)

Self-Refining Training for Amortized Density Functional Theory. - Majdi Hassan; Cristian Gabellini; Hatem Helal; Dominique Beaini; Kirill Neklyudov (CoRR, 2025)

Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities. - Tara Akhound-Sadegh; Jungyoon Lee; Avishek Joey Bose; Valentin De Bortoli; Arnaud Doucet; Michael M. Bronstein; Dominique Beaini; Siamak Ravanbakhsh; Kirill Neklyudov; Alexander Tong 001 (CoRR, 2025)