1 code implementation • ECCV 2020 • Luca Cosmo, Giorgia Minello, Michael Bronstein, Luca Rossi, Andrea Torsello

We introduce the Average Mixing Kernel Signature (AMKS), a novel signature for points on non-rigid three-dimensional shapes based on the average mixing kernel and continuous-time quantum walks.

1 code implementation • 24 Oct 2024 • Linus Bao, Emily Jin, Michael Bronstein, İsmail İlkan Ceylan, Matthias Lanzinger

Graph Transformers are popular neural networks that extend the well-known Transformer architecture to the graph domain.

no code implementations • 23 Oct 2024 • Ahmed A. Elhag, T. Konstantin Rusch, Francesco Di Giovanni, Michael Bronstein

By formulating equivariance as a new learning objective, we can control the level of approximate equivariance in the model.

no code implementations • 10 Oct 2024 • Jarrid Rector-Brooks, Mohsin Hasan, Zhangzhi Peng, Zachary Quinn, Chenghao Liu, Sarthak Mittal, Nouha Dziri, Michael Bronstein, Yoshua Bengio, Pranam Chatterjee, Alexander Tong, Avishek Joey Bose

Generative modeling of discrete data underlies important applications spanning text-based agents like ChatGPT to the design of the very building blocks of life in protein sequences.

no code implementations • 25 Aug 2024 • Haitz Sáez de Ocáriz Borde, Anastasis Kratsios, Marc T. Law, Xiaowen Dong, Michael Bronstein

NSTs are implemented as three neural networks trained in an end-to-end manner: an embedding network, which learns to optimize the location of nodes as events in the spacetime manifold, and two other networks that optimize the space and time geometries in parallel, which we call a neural (quasi-)metric and a neural partial order, respectively.

no code implementations • 10 Aug 2024 • Yam Eitan, Yoav Gelberg, Guy Bar-Shalom, Fabrizio Frasca, Michael Bronstein, Haggai Maron

Given the significant expressivity limitations of MPNNs, our paper aims to explore both the strengths and weaknesses of HOMP's expressive power and subsequently design novel architectures to address these limitations.

no code implementations • 8 Aug 2024 • Zifeng Ding, Yifeng Li, Yuan He, Antonio Norelli, Jingcheng Wu, Volker Tresp, Yunpu Ma, Michael Bronstein

Learning useful representations for continuous-time dynamic graphs (CTDGs) is challenging, due to the concurrent need to span long node interaction histories and grasp nuanced temporal details.

no code implementations • 24 Jun 2024 • Baskaran Sripathmanathan, Xiaowen Dong, Michael Bronstein

We further show that at sufficiently low SNRs, for LS reconstruction we have a $\Lambda$-shaped error curve and for GLR reconstruction, a sample size of $ \mathcal{O}(\sqrt{N})$, where $N$ is the total number of vertices, results in lower reconstruction error than near full observation.

1 code implementation • 31 May 2024 • Ben Finkelshtein, İsmail İlkan Ceylan, Michael Bronstein, Ron Levie

We then devise an efficient graph learning algorithm operating directly on ICG in linear memory and time with respect to the number of nodes (rather than edges).

1 code implementation • 30 May 2024 • Jianan Zhao, Hesham Mostafa, Mikhail Galkin, Michael Bronstein, Zhaocheng Zhu, Jian Tang

Traditional graph ML models such as graph neural networks (GNNs) trained on graphs cannot perform inference on a new graph with feature and label spaces different from the training ones.

no code implementations • 30 May 2024 • Guillaume Huguet, James Vuckovic, Kilian Fatras, Eric Thibodeau-Laufer, Pablo Lemos, Riashat Islam, Cheng-Hao Liu, Jarrid Rector-Brooks, Tara Akhound-Sadegh, Michael Bronstein, Alexander Tong, Avishek Joey Bose

Proteins are essential for almost all biological processes and derive their diverse functions from complex 3D structures, which are in turn determined by their amino acid sequences.

no code implementations • 24 May 2024 • Jacob Bamberger, Federico Barbero, Xiaowen Dong, Michael Bronstein

The dominant paradigm for learning on graph-structured data is message passing.

no code implementations • 23 May 2024 • Kacper Kapusniak, Peter Potaptchik, Teodora Reu, Leo Zhang, Alexander Tong, Michael Bronstein, Avishek Joey Bose, Francesco Di Giovanni

Matching objectives underpin the success of modern generative models and rely on constructing conditional paths that transform a source distribution into a target distribution.

no code implementations • 23 May 2024 • Oscar Davis, Samuel Kessler, Mircea Petrache, İsmail İlkan Ceylan, Michael Bronstein, Avishek Joey Bose

Generative modeling over discrete data has recently seen numerous success stories, with applications spanning language modeling, biological sequence design, and graph-structured molecular data.

no code implementations • 22 May 2024 • Joshua Southern, Francesco Di Giovanni, Michael Bronstein, Johannes F. Lutzeyer

Consequently, we propose a variant of VN with the same computational complexity, which can have different sensitivity to nodes based on the graph structure.

no code implementations • 2 May 2024 • Maksym Korablyov, Cheng-Hao Liu, Moksh Jain, Almer M. van der Sloot, Eric Jolicoeur, Edward Ruediger, Andrei Cristian Nica, Emmanuel Bengio, Kostiantyn Lapchevskyi, Daniel St-Cyr, Doris Alexandra Schuetz, Victor Ion Butoi, Jarrid Rector-Brooks, Simon Blackburn, Leo Feng, Hadi Nekoei, SaiKrishna Gottipati, Priyesh Vijayan, Prateek Gupta, Ladislav Rampášek, Sasikanth Avancha, Pierre-Luc Bacon, William L. Hamilton, Brooks Paige, Sanchit Misra, Stanislaw Kamil Jastrzebski, Bharat Kaul, Doina Precup, José Miguel Hernández-Lobato, Marwin Segler, Michael Bronstein, Anne Marinier, Mike Tyers, Yoshua Bengio

Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a significant challenge.

no code implementations • 14 Feb 2024 • Theodore Papamarkou, Tolga Birdal, Michael Bronstein, Gunnar Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Liò, Paolo Di Lorenzo, Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T. Schaub, Petar Veličković, Bei Wang, Yusu Wang, Guo-Wei Wei, Ghada Zamzmi

At the same time, this paper serves as an invitation to the scientific community to actively participate in TDL research to unlock the potential of this emerging field.

3 code implementations • 13 Feb 2024 • Emily Jin, Michael Bronstein, İsmail İlkan Ceylan, Matthias Lanzinger

A large body of work has investigated the properties of graph neural networks and identified several limitations, particularly pertaining to their expressive power.

no code implementations • 3 Feb 2024 • Christopher Morris, Fabrizio Frasca, Nadav Dym, Haggai Maron, İsmail İlkan Ceylan, Ron Levie, Derek Lim, Michael Bronstein, Martin Grohe, Stefanie Jegelka

Machine learning on graphs, especially using graph neural networks (GNNs), has seen a surge in interest due to the wide availability of graph data across a broad spectrum of disciplines, from life to social and engineering sciences.

1 code implementation • 12 Dec 2023 • Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D. Malliaros, Taco Cohen, Pietro Liò, Yoshua Bengio, Michael Bronstein

In these graphs, the geometric attributes transform according to the inherent physical symmetries of 3D atomic systems, including rotations and translations in Euclidean space, as well as node permutations.

no code implementations • 22 Oct 2023 • Floor Eijkelboom, Erik Bekkers, Michael Bronstein, Francesco Di Giovanni

This suggests that the importance of message passing is limited when the model can construct strong structural encodings.

no code implementations • 10 Oct 2023 • Qitian Wu, Chenxiao Yang, Kaipeng Zeng, Fan Nie, Michael Bronstein, Junchi Yan

Graph diffusion equations are intimately related to graph neural networks (GNNs) and have recently attracted attention as a principled framework for analyzing GNN dynamics, formalizing their expressive power, and justifying architectural choices.

1 code implementation • 3 Oct 2023 • Avishek Joey Bose, Tara Akhound-Sadegh, Guillaume Huguet, Kilian Fatras, Jarrid Rector-Brooks, Cheng-Hao Liu, Andrei Cristian Nica, Maksym Korablyov, Michael Bronstein, Alexander Tong

The computational design of novel protein structures has the potential to impact numerous scientific disciplines greatly.

no code implementations • 2 Oct 2023 • Federico Barbero, Ameya Velingker, Amin Saberi, Michael Bronstein, Francesco Di Giovanni

Graph Neural Networks (GNNs) are popular models for machine learning on graphs that typically follow the message-passing paradigm, whereby the feature of a node is updated recursively upon aggregating information over its neighbors.

2 code implementations • 2 Oct 2023 • Ben Finkelshtein, Xingyue Huang, Michael Bronstein, İsmail İlkan Ceylan

Graph neural networks are popular architectures for graph machine learning, based on iterative computation of node representations of an input graph through a series of invariant transformations.

1 code implementation • 2 Oct 2023 • Jianan Zhao, Le Zhuo, Yikang Shen, Meng Qu, Kai Liu, Michael Bronstein, Zhaocheng Zhu, Jian Tang

Furthermore, GraphText paves the way for interactive graph reasoning, allowing both humans and LLMs to communicate with the model seamlessly using natural language.

1 code implementation • 30 Aug 2023 • Ilia Igashov, Arne Schneuing, Marwin Segler, Michael Bronstein, Bruno Correia

Retrosynthesis planning is a fundamental challenge in chemistry which aims at designing reaction pathways from commercially available starting materials to a target molecule.

1 code implementation • 17 Jul 2023 • Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik Bekkers, Michael Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess Smidt, Shuiwang Ji

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.

5 code implementations • NeurIPS 2023 • Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, Matthias Fey, Weihua Hu, Emanuele Rossi, Jure Leskovec, Michael Bronstein, Guillaume Rabusseau, Reihaneh Rabbany

We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning models on temporal graphs.

no code implementations • 1 Jul 2023 • Baskaran Sripathmanathan, Xiaowen Dong, Michael Bronstein

We show that under the setting of noisy observation and least-squares reconstruction this is not always the case, characterising the behaviour both theoretically and experimentally.

no code implementations • 25 May 2023 • Claudio Battiloro, Indro Spinelli, Lev Telyatnikov, Michael Bronstein, Simone Scardapane, Paolo Di Lorenzo

Latent Graph Inference (LGI) relaxed the reliance of Graph Neural Networks (GNNs) on a given graph topology by dynamically learning it.

1 code implementation • 17 May 2023 • Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael Bronstein

Graph Neural Networks (GNNs) have become the de-facto standard tool for modeling relational data.

Ranked #1 on Node Classification on Non-Homophilic (Heterophilic) Graphs on Chameleon (48%/32%/20% fixed splits)

Graph Neural Network Node Classification on Non-Homophilic (Heterophilic) Graphs

1 code implementation • 13 May 2023 • Benjamin Gutteridge, Xiaowen Dong, Michael Bronstein, Francesco Di Giovanni

Message passing neural networks (MPNNs) have been shown to suffer from the phenomenon of over-squashing that causes poor performance for tasks relying on long-range interactions.

Ranked #3 on Graph Classification on Peptides-func

1 code implementation • 6 Feb 2023 • Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio', Michael Bronstein

Our analysis provides a unified framework to study different recent methods introduced to cope with over-squashing and serves as a justification for a class of methods that fall under graph rewiring.

1 code implementation • NeurIPS 2023 • Joshua Southern, Jeremy Wayland, Michael Bronstein, Bastian Rieck

Graph generative model evaluation necessitates understanding differences between graphs on the distributional level.

2 code implementations • 24 Oct 2022 • Arne Schneuing, Charles Harris, Yuanqi Du, Kieran Didi, Arian Jamasb, Ilia Igashov, Weitao Du, Carla Gomes, Tom Blundell, Pietro Lio, Max Welling, Michael Bronstein, Bruno Correia

Here we show how a single pre-trained diffusion model can be applied to a broader range of problems, such as off-the-shelf property optimization, explicit negative design, and partial molecular design with inpainting.

1 code implementation • 11 Oct 2022 • Ilia Igashov, Hannes Stärk, Clément Vignac, Victor Garcia Satorras, Pascal Frossard, Max Welling, Michael Bronstein, Bruno Correia

Additionally, the model automatically determines the number of atoms in the linker and its attachment points to the input fragments.

no code implementations • 4 Oct 2022 • Edoardo Cetin, Benjamin Chamberlain, Michael Bronstein, Jonathan J Hunt

We propose a new class of deep reinforcement learning (RL) algorithms that model latent representations in hyperbolic space.

1 code implementation • 17 Jun 2022 • Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde, Michael Bronstein, Petar Veličković, Pietro Liò

A Sheaf Neural Network (SNN) is a type of Graph Neural Network (GNN) that operates on a sheaf, an object that equips a graph with vector spaces over its nodes and edges and linear maps between these spaces.

Ranked #10 on Node Classification on Wisconsin

no code implementations • 14 Feb 2022 • Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderon, Michael Bronstein, Marcello Restelli

In this setting, the agent can take a finite amount of reward-free interactions from a subset of these environments.

no code implementations • 2 Feb 2022 • Francesco Di Giovanni, Giulia Luise, Michael Bronstein

Graph embeddings, wherein the nodes of the graph are represented by points in a continuous space, are used in a broad range of Graph ML applications.

no code implementations • 14 Dec 2021 • Luca Cosmo, Giorgia Minello, Alessandro Bicciato, Michael Bronstein, Emanuele Rodolà, Luca Rossi, Andrea Torsello

The convolution operator at the core of many modern neural architectures can effectively be seen as performing a dot product between an input matrix and a filter.

1 code implementation • 23 Nov 2021 • Emanuele Rossi, Henry Kenlay, Maria I. Gorinova, Benjamin Paul Chamberlain, Xiaowen Dong, Michael Bronstein

While Graph Neural Networks (GNNs) have recently become the de facto standard for modeling relational data, they impose a strong assumption on the availability of the node or edge features of the graph.

no code implementations • 28 Sep 2021 • Balder Croquet, Daan Christiaens, Seth M. Weinberg, Michael Bronstein, Dirk Vandermeulen, Peter Claes

The one-step registration model is benchmarked against iterative techniques, trading in a slightly lower performance in terms of shape fit for a higher compactness.

2 code implementations • NeurIPS 2021 • Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yu Guang Wang, Pietro Liò, Guido Montúfar, Michael Bronstein

Nevertheless, these models can be severely constrained by the rigid combinatorial structure of Simplicial Complexes (SCs).

Ranked #1 on Graph Regression on ZINC 100k

2 code implementations • ICLR Workshop GTRL 2021 • Cristian Bodnar, Fabrizio Frasca, Yu Guang Wang, Nina Otter, Guido Montúfar, Pietro Liò, Michael Bronstein

The pairwise interaction paradigm of graph machine learning has predominantly governed the modelling of relational systems.

no code implementations • NeurIPS 2020 • Jean Feydy, Joan Glaunès, Benjamin Charlier, Michael Bronstein

Geometric methods rely on tensors that can be encoded using a symbolic formula and data arrays, such as kernel and distance matrices.

no code implementations • 26 Nov 2020 • Or Litany, Emanuele Rodolà, Alex Bronstein, Michael Bronstein, Daniel Cremers

We assume to be given a reference shape and its multiple parts undergoing a non-rigid deformation.

no code implementations • 10 Sep 2020 • Soha Sadat Mahdi, Nele Nauwelaers, Philip Joris, Giorgos Bouritsas, Shunwang Gong, Sergiy Bokhnyak, Susan Walsh, Mark D. Shriver, Michael Bronstein, Peter Claes, .

Since embeddings are accepted as an input, there is no need to train classifiers for the different properties and available data can be used more efficiently.

no code implementations • 31 Jul 2020 • Xiaowen Dong, Dorina Thanou, Laura Toni, Michael Bronstein, Pascal Frossard

The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains such as networks and graphs, are one of the key questions in modern machine learning.

10 code implementations • 18 Jun 2020 • Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, Michael Bronstein

Graph Neural Networks (GNNs) have recently become increasingly popular due to their ability to learn complex systems of relations or interactions arising in a broad spectrum of problems ranging from biology and particle physics to social networks and recommendation systems.

no code implementations • 28 Apr 2020 • Luca Belli, Sofia Ira Ktena, Alykhan Tejani, Alexandre Lung-Yut-Fon, Frank Portman, Xiao Zhu, Yuanpu Xie, Akshay Gupta, Michael Bronstein, Amra Delić, Gabriele Sottocornola, Walter Anelli, Nazareno Andrade, Jessie Smith, Wenzhe Shi

Recommender systems constitute the core engine of most social network platforms nowadays, aiming to maximize user satisfaction along with other key business objectives.

5 code implementations • 23 Apr 2020 • Fabrizio Frasca, Emanuele Rossi, Davide Eynard, Ben Chamberlain, Michael Bronstein, Federico Monti

Graph representation learning has recently been applied to a broad spectrum of problems ranging from computer graphics and chemistry to high energy physics and social media.

Ranked #5 on Node Classification on AMZ Comp

3 code implementations • CVPR 2020 • Dominik Kulon, Riza Alp Güler, Iasonas Kokkinos, Michael Bronstein, Stefanos Zafeiriou

We introduce a simple and effective network architecture for monocular 3D hand pose estimation consisting of an image encoder followed by a mesh convolutional decoder that is trained through a direct 3D hand mesh reconstruction loss.

Ranked #22 on 3D Hand Pose Estimation on FreiHAND

no code implementations • 27 Mar 2020 • Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi, Nassir Navab, Michael Bronstein

Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful machine learning tool for Computer-Aided Diagnosis (CADx) and disease prediction.

1 code implementation • 11 Feb 2020 • Anees Kazi, Luca Cosmo, Seyed-Ahmad Ahmadi, Nassir Navab, Michael Bronstein

We provide an extensive evaluation of applications from the domains of healthcare (disease prediction), brain imaging (age prediction), computer graphics (3D point cloud segmentation), and computer vision (zero-shot learning).

no code implementations • 16 Jan 2020 • Guadalupe Gonzalez, Shunwang Gong, Ivan Laponogov, Kirill Veselkov, Michael Bronstein

Recent research efforts have shown the possibility to discover anticancer drug-like molecules in food from their effect on protein-protein interaction networks, opening a potential pathway to disease-beating diet design.

1 code implementation • 13 Nov 2019 • Shunwang Gong, Lei Chen, Michael Bronstein, Stefanos Zafeiriou

Intrinsic graph convolution operators with differentiable kernel functions play a crucial role in analyzing 3D shape meshes.

1 code implementation • 16 May 2019 • Emanuele Rossi, Federico Monti, Michael Bronstein, Pietro Liò

Non-coding RNA (ncRNA) are RNA sequences which don't code for a gene but instead carry important biological functions.

2 code implementations • ICCV 2019 • Giorgos Bouritsas, Sergiy Bokhnyak, Stylianos Ploumpis, Michael Bronstein, Stefanos Zafeiriou

Generative models for 3D geometric data arise in many important applications in 3D computer vision and graphics.

1 code implementation • 4 May 2019 • Dominik Kulon, Haoyang Wang, Riza Alp Güler, Michael Bronstein, Stefanos Zafeiriou

In this paper, we demonstrate an alternative solution that is based on the idea of encoding images into a latent non-linear representation of meshes.

no code implementations • 25 Mar 2019 • Shiyang Cheng, Michael Bronstein, Yuxiang Zhou, Irene Kotsia, Maja Pantic, Stefanos Zafeiriou

Generative Adversarial Networks (GANs) are currently the method of choice for generating visual data.

no code implementations • CVPR 2018 • Or Litany, Alex Bronstein, Michael Bronstein, Ameesh Makadia

In this work, we propose a novel learning-based method for the completion of partial shapes.

1 code implementation • 25 Jul 2017 • Zorah Lähner, Matthias Vestner, Amit Boyarski, Or Litany, Ron Slossberg, Tal Remez, Emanuele Rodolà, Alex Bronstein, Michael Bronstein, Ron Kimmel, Daniel Cremers

We present a method to match three dimensional shapes under non-isometric deformations, topology changes and partiality.

no code implementations • ICCV 2015 • Nauman Shahid, Vassilis Kalofolias, Xavier Bresson, Michael Bronstein, Pierre Vandergheynst

Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering.

2 code implementations • 7 Aug 2014 • Vassilis Kalofolias, Xavier Bresson, Michael Bronstein, Pierre Vandergheynst

Our main goal is thus to find a low-rank solution that is structured by the proximities of rows and columns encoded by graphs.

Ranked #15 on Recommendation Systems on MovieLens 100K (using extra training data)

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