Prudencio Tossou

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All published works

Action	Title	Year	Authors
+ PDF Chat	Implicit Delta Learning of High Fidelity Neural Network Potentials	2024	Stephan Thaler Cristian Gabellini Nikhil Shenoy Prudencio Tossou
+ PDF Chat	OpenQDC: Open Quantum Data Commons	2024	Cristian Gabellini Nikhil Shenoy Stephan Thaler Semih Cantürk Daniel McNeela Dominique Beaini Michael Bronstein Prudencio Tossou
+ PDF Chat	Gotta be SAFE: a new framework for molecular design	2024	Emmanuel Noutahi Cristian Gabellini Michael Craig Jonathan S. C. Lim Prudencio Tossou
+	Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets	2023	Dominique Beaini Shenyang Huang Joao Alex Cunha Gabriela Moisescu-Pareja Oleksandr Dymov Samuel Maddrell-Mander Callum McLean Frederik Wenkel Luis T. Díaz Müller Jama Hussein Mohamud
+	Gotta be SAFE: A New Framework for Molecular Design	2023	Emmanuel Noutahi Cristian Gabellini Michael Craig Jonathan S. C. Lim Prudencio Tossou
+	Role of Structural and Conformational Diversity for Machine Learning Potentials	2023	Nikhil Shenoy Prudencio Tossou Emmanuel Noutahi Hadrien Mary Dominique Beaini Jiarui Ding
+	Rethinking Graph Transformers with Spectral Attention	2021	Devin Kreuzer Dominique Beaini William L. Hamilton Vincent Létourneau Prudencio Tossou
+	3D Infomax improves GNNs for Molecular Property Prediction	2021	H. Stärk Dominique Beaini Gabriele Corso Prudencio Tossou Christian Dallago Stephan Günnemann Píetro Lió
+	3D Infomax improves GNNs for Molecular Property Prediction	2021	H. Stärk Dominique Beaini Gabriele Corso Prudencio Tossou Christian Dallago Stephan Günnemann Píetro Lió
+	Rethinking Graph Transformers with Spectral Attention	2021	Devin Kreuzer Dominique Beaini William L. Hamilton Vincent Létourneau Prudencio Tossou
+	3D Infomax improves GNNs for Molecular Property Prediction	2021	H. Stärk Dominique Beaini Gabriele Corso Prudencio Tossou Christian Dallago Stephan Günnemann Píetro Lió
+	Rethinking Graph Transformers with Spectral Attention	2021	Devin Kreuzer Dominique Beaini William L. Hamilton Vincent Létourneau Prudencio Tossou
+	Geodesics in fibered latent spaces: A geometric approach to learning correspondences between conditions	2020	Tariq Daouda Reda Chhaibi Prudencio Tossou Alexandra‐Chloé Villani
+	Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling.	2019	Emmanuel Noutahi Dominique Beaini Julien Horwood Prudencio Tossou
+	Adaptive Deep Kernel Learning	2019	Prudencio Tossou Basile Dura François Laviolette Mario Marchand Alexandre Lacoste
+	Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling	2019	Emmanuel Noutahi Dominique Beaini Julien Horwood Sébastien Giguère Prudencio Tossou

Common Coauthors

Coauthor	Papers Together
Dominique Beaini	11
Emmanuel Noutahi	5
Cristian Gabellini	5
Devin Kreuzer	3
Vincent Létourneau	3
Stephan Günnemann	3
William L. Hamilton	3
Michael Craig	3
Nikhil Shenoy	3
Píetro Lió	3
H. Stärk	3
Christian Dallago	3
Stephan Thaler	2
Julien Horwood	2
Hadrien Mary	2
Gabriele Corso	2
Jonathan S. C. Lim	2
Ali Parviz	1
François Laviolette	1
Mirco Ravanelli	1
Maciej Sypetkowski	1
Gabriele Corso	1
Błażej Banaszewski	1
Dominic Masters	1
Michael Bronstein	1
Alexandre Lacoste	1
Ioannis Koutis	1
Sébastien Giguère	1
Zhaocheng Zhu	1
Basile Dura	1
Kerstin Kläser	1
Mario Marchand	1
Michał Koziarski	1
Frederik Wenkel	1
Jama Hussein Mohamud	1
Jian Tang	1
Guillaume Rabusseau	1
Tariq Daouda	1
Semih Cantürk	1
Christopher G. Morris	1
Reda Chhaibi	1
Andrew Fitzgibbon	1
Guy Wolf	1
Josef Dean	1
Cas Wognum	1
Gabriela Moisescu-Pareja	1
Therence Bois	1
Jiarui Lu	1
Joao Alex Cunha	1
Shenyang Huang	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Junction Tree Variational Autoencoder for Molecular Graph Generation	2018	Wengong Jin Regina Barzilay Tommi Jaakkola	2
+	Representation Learning with Contrastive Predictive Coding	2018	Aäron van den Oord Yazhe Li Oriol Vinyals	2
+	Inductive Representation Learning on Large Graphs	2017	William L. Hamilton Rex Ying Jure Leskovec	2
+ PDF Chat	Multi-objective de novo drug design with conditional graph generative model	2018	Yibo Li Liangren Zhang Zhenming Liu	2
+	Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks	2019	Minjie Wang Da Zheng Zihao Ye Quan Gan Mufei Li Xiang Song Jinjing Zhou Chao Ma Lingfan Yu Yu Gai	2
+	How Powerful are Graph Neural Networks	2018	Keyulu Xu Weihua Hu Jure Leskovec Stefanie Jegelka	2
+	Neural Message Passing for Quantum Chemistry	2017	Justin Gilmer Samuel S. Schoenholz Patrick Riley Oriol Vinyals George E. Dahl	2
+	Learning Deep Generative Models of Graphs	2018	Yujia Li Oriol Vinyals Chris Dyer Razvan Pascanu Peter Battaglia	2
+	Gated Graph Sequence Neural Networks	2016	Yujia Li Daniel Tarlow Marc Brockschmidt Richard S. Zemel	1
+	From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification	2016	André F. T. Martins Ramón Fernández Astudillo	1
+ PDF Chat	Molecular graph convolutions: moving beyond fingerprints	2016	Steven Kearnes Kevin McCloskey Marc Berndl Vijay S. Pande Patrick Riley	1
+	Semi-Supervised Classification with Graph Convolutional Networks	2016	Thomas Kipf Max Welling	1
+ PDF Chat	Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs	2017	Federico Monti Davide Boscaini Jonathan Masci Emanuele Rodolà Jan Svoboda Michael M. Bronstein	1
+ PDF Chat	Geometric Deep Learning: Going beyond Euclidean data	2017	Michael M. Bronstein Joan Bruna Yann LeCun Arthur Szlam Pierre Vandergheynst	1
+ PDF Chat	MoleculeNet: a benchmark for molecular machine learning	2017	Zhenqin Wu Bharath Ramsundar Evan N. Feinberg Joseph Gomes Caleb Geniesse Aneesh Pappu Karl Leswing Vijay S. Pande	1
+	Axiomatic Attribution for Deep Networks	2017	Mukund Sundararajan Ankur Taly Qiqi Yan	1
+	Prototypical Networks for Few-shot Learning	2017	Jake Snell Kevin Swersky Richard S. Zemel	1
+	Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks	2017	Chelsea Finn Pieter Abbeel Sergey Levine	1
+	Proximal Policy Optimization Algorithms	2017	John Schulman Filip Wolski Prafulla Dhariwal Alec Radford Oleg Klimov	1
+	Meta-SGD: Learning to Learn Quickly for Few-Shot Learning	2017	Zhenguo Li Fengwei Zhou Fei Chen Hang Li	1
+	Graph Attention Networks	2017	Petar Veličković Guillem Cucurull Arantxa Casanova Adriana Romero Píetro Lió Yoshua Bengio	1
+	Wasserstein Auto-Encoders	2017	Ilya Tolstikhin Olivier Bousquet Sylvain Gelly Bernhard Schoelkopf	1
+	Residual Gated Graph ConvNets	2017	Xavier Bresson Thomas Laurent	1
+	Few-Shot Learning with Graph Neural Networks	2017	Víctor García Joan Bruna	1
+	MINE: Mutual Information Neural Estimation.	2018	Ishmael Belghazi Sai Rajeswar Aristide Baratin R Devon Hjelm Aaron Courville	1
+	Conditional Neural Processes	2018	Marta Garnelo Dan Rosenbaum Chris J. Maddison Tiago Ramalho David Saxton Murray Shanahan Yee Whye Teh Danilo Jimenez Rezende S. M. Ali Eslami	1
+	Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation	2018	Jiaxuan You Bowen Liu Rex Ying Vijay S. Pande Jure Leskovec	1
+	Bayesian Model-Agnostic Meta-Learning	2018	Taesup Kim Jaesik Yoon Ousmane Dia Sungwoong Kim Yoshua Bengio Sungjin Ahn	1
+	MolGAN: An implicit generative model for small molecular graphs	2018	Nicola De Cao Thomas Kipf	1
+	Learning deep representations by mutual information estimation and maximization	2018	R Devon Hjelm Alex Fedorov Samuel Lavoie-Marchildon Karan Grewal Phil Bachman Adam Trischler Yoshua Bengio	1
+	DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation	2018	Rim Assouel Mohamed M. Ahmed Marwin Segler Amir Saffari Yoshua Bengio	1
+	Attentive Neural Processes	2019	Hyunjik Kim Andriy Mnih Jonathan Schwarz Marta Garnelo Ali Eslami Dan Rosenbaum Oriol Vinyals Yee Whye Teh	1
+	A Closer Look at Few-shot Classification	2019	Wei-Yu Chen Yen‐Cheng Liu Zsolt Kira Yu-Chiang Frank Wang Jia‐Bin Huang	1
+	Fast Graph Representation Learning with PyTorch Geometric	2019	Matthias Fey Jan Eric Lenssen	1
+ PDF Chat	PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges	2019	Oliver T. Unke Markus Meuwly	1
+	Few-shot Learning: A Survey	2019	Yaqing Wang Quanming Yao	1
+	Graph Convolutional Networks with EigenPooling	2019	Yao Ma Suhang Wang Charų C. Aggarwal Jiliang Tang	1
+	MoleculeNet: A Benchmark for Molecular Machine Learning	2017	Zhenqin Wu Bharath Ramsundar Evan N. Feinberg Joseph Gomes Caleb Geniesse Aneesh Pappu Karl Leswing Vijay S. Pande	1
+	Predict then Propagate: Graph Neural Networks meet Personalized PageRank	2018	Johannes Klicpera Aleksandar Bojchevski Stephan Günnemann	1
+	Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization	2019	Pengfei Chen Weiwen Liu Chang‐Yu Hsieh Guangyong Chen Shengyu Zhang	1
+	Neural Turing Machines	2014	Alex Graves Greg Wayne Ivo Danihelka	1
+	GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models	2018	Jiaxuan You Rex Ying Xiang Ren William L. Hamilton Jure Leskovec	1
+	Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination	2018	Zhirong Wu Yuanjun Xiong Stella X. Yu Dahua Lin	1
+	Neural Message Passing for Quantum Chemistry	2017	Justin Gilmer Samuel S. Schoenholz Patrick Riley Oriol Vinyals George E. Dahl	1
+	Deep Sets	2017	Manzil Zaheer Satwik Kottur Siamak Ravanbakhsh Barnabás Póczos Ruslan Salakhutdinov Alexander J. Smola	1
+	Partial Functional Correspondence	2015	Emanuele Rodolà Luca Cosmo Michael M. Bronstein Andrea Torsello Daniel Cremers	1
+ PDF Chat	Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks	2019	Christopher Morris Martin Ritzert Matthias Fey William L. Hamilton Jan Eric Lenssen Gaurav Rattan Martin Grohe	1
+	Explanation in artificial intelligence: Insights from the social sciences	2018	Tim Miller	1
+	Matching networks for one shot learning	2016	Oriol Vinyals Charles Blundell Timothy Lillicrap Koray Kavukcuoglu Daan Wierstra	1
+	Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation	2018	Matt Post David Vilar	1