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ORCIDDominique Beaini
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2020 – today
- 2024
[c11]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. CVPR 2024: 11757-11768- [c10]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 - [c9]Semih Cantürk, Renming Liu, Olivier Lapointe-Gagné, Vincent Létourneau, Guy Wolf, Dominique Beaini, Ladislav Rampásek:
Graph Positional and Structural Encoder. ICML 2024 - [i24]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 abs/2404.10242 (2024) - [i23]Maciej Sypetkowski, Frederik Wenkel, Farimah Poursafaei, Nia Dickson, Karush Suri, Philip Fradkin, Dominique Beaini:
On the Scalability of GNNs for Molecular Graphs. CoRR abs/2404.11568 (2024) - [i22]Philip Fradkin, Puria Azadi, Karush Suri, Frederik Wenkel, Ali Bashashati, Maciej Sypetkowski, Dominique Beaini:
How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval. CoRR abs/2409.08302 (2024) - [i21]Majdi Hassan, Nikhil Shenoy, Jungyoon Lee, Hannes Stärk, Stephan Thaler, Dominique Beaini:
ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation. CoRR abs/2410.22388 (2024) - 2023
- [j3]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) - [c8]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: i-xix - [c7]Alexander Mathiasen, Hatem Helal, Kerstin Klaser, Paul Balanca, Josef Dean, Carlo Luschi, Dominique Beaini, Andrew W. Fitzgibbon, Dominic Masters:
Generating QM1B with PySCFIPU. NeurIPS 2023 - [i20]Xiangyu Zhao, Hannes Stärk, Dominique Beaini, Pietro Liò, Yiren Zhao:
Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration. CoRR abs/2301.11517 (2023) - [i19]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 abs/2302.02947 (2023) - [i18]Renming Liu, Semih Cantürk, Olivier Lapointe-Gagné, Vincent Létourneau, Guy Wolf, Dominique Beaini, Ladislav Rampásek:
Graph Positional and Structural Encoder. CoRR abs/2307.07107 (2023) - [i17]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 abs/2310.04292 (2023) - [i16]Nikhil Shenoy, Prudencio Tossou, Emmanuel Noutahi, Hadrien Mary, Dominique Beaini, Jiarui Ding:
Role of Structural and Conformational Diversity for Machine Learning Potentials. CoRR abs/2311.00862 (2023) - [i15]Alexander Mathiasen, Hatem Helal, Kerstin Klaser, Paul Balanca, Josef Dean, Carlo Luschi, Dominique Beaini, Andrew W. Fitzgibbon, Dominic Masters:
Generating QM1B with PySCFIPU. CoRR abs/2311.01135 (2023) - 2022
- [c6]Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Lió:
3D Infomax improves GNNs for Molecular Property Prediction. ICML 2022: 20479-20502 - [c5]Vijay Prakash Dwivedi, Ladislav Rampásek, Michael Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, Dominique Beaini:
Long Range Graph Benchmark. NeurIPS 2022 - [c4]Ladislav Rampásek, Michael Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, Dominique Beaini:
Recipe for a General, Powerful, Scalable Graph Transformer. NeurIPS 2022 - [i14]Ladislav Rampásek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, Dominique Beaini:
Recipe for a General, Powerful, Scalable Graph Transformer. CoRR abs/2205.12454 (2022) - [i13]Vijay Prakash Dwivedi, Ladislav Rampásek, Mikhail Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, Dominique Beaini:
Long Range Graph Benchmark. CoRR abs/2206.08164 (2022) - [i12]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 abs/2212.02229 (2022) - 2021
- [j2]Dominique Beaini
, Sofiane Achiche, Alexandre Duperré, Maxime Raison:
Deep green function convolution for improving saliency in convolutional neural networks. Vis. Comput. 37(2): 227-244 (2021) - [c3]Dominique Beaini, Saro Passaro, Vincent Létourneau, William L. Hamilton, Gabriele Corso, Pietro Lió:
Directional Graph Networks. ICML 2021: 748-758 - [c2]Devin Kreuzer, Dominique Beaini, William L. Hamilton, Vincent Létourneau, Prudencio Tossou:
Rethinking Graph Transformers with Spectral Attention. NeurIPS 2021: 21618-21629 - [i11]Devin Kreuzer, Dominique Beaini, William L. Hamilton, Vincent Létourneau, Prudencio Tossou:
Rethinking Graph Transformers with Spectral Attention. CoRR abs/2106.03893 (2021) - [i10]Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Liò:
3D Infomax improves GNNs for Molecular Property Prediction. CoRR abs/2110.04126 (2021) - 2020
- [c1]Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, Petar Velickovic:
Principal Neighbourhood Aggregation for Graph Nets. NeurIPS 2020 - [i9]Dominique Beaini, Sofiane Achiche, Alexandre Duperré, Maxime Raison:
Saliency Enhancement using Gradient Domain Edges Merging. CoRR abs/2002.04380 (2020) - [i8]Dominique Beaini, Sofiane Achiche, Maxime Raison:
Improving Convolutional Neural Networks Via Conservative Field Regularisation and Integration. CoRR abs/2003.05182 (2020) - [i7]Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, Petar Velickovic:
Principal Neighbourhood Aggregation for Graph Nets. CoRR abs/2004.05718 (2020) - [i6]Dominique Beaini, Saro Passaro, Vincent Létourneau, William L. Hamilton, Gabriele Corso, Pietro Liò:
Directional Graph Networks. CoRR abs/2010.02863 (2020)
2010 – 2019
- 2019
- [i5]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 abs/1902.00176 (2019) - [i4]Emmanuel Noutahi, Dominique Beaini, Julien Horwood, Prudencio Tossou:
Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling. CoRR abs/1905.11577 (2019) - [i3]Dominique Beaini, Sofiane Achiche, Alexandre Duperré, Maxime Raison:
Deep Green Function Convolution for Improving Saliency in Convolutional Neural Networks. CoRR abs/1908.08331 (2019) - 2018
- [i2]Dominique Beaini, Sofiane Achiche, Fabrice Nonez, Maxime Raison:
Computing the Spatial Probability of Inclusion inside Partial Contours for Computer Vision Applications. CoRR abs/1806.01339 (2018) - [i1]Dominique Beaini, Sofiane Achiche, Yann-Seing Law-Kam Cio, Maxime Raison:
Novel Convolution Kernels for Computer Vision and Shape Analysis based on Electromagnetism. CoRR abs/1806.07996 (2018) - 2017
- [j1]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: 33-44 (2017)
Coauthor Index
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last updated on 2024-12-01 00:17 CET by the dblp team
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