Michael Bronstein

research

∙ 08/30/2023

RetroBridge: Modeling Retrosynthesis with Markov Bridges

Retrosynthesis planning is a fundamental challenge in chemistry which ai...

0 Ilia Igashov, et al. ∙

research

∙ 07/07/2023

Social learning across symbolic cultural barriers in non-human cultures

Social learning is key in the development of both human and non-human an...

0 António Leitão, et al. ∙

research

∙ 07/03/2023

Temporal Graph Benchmark for Machine Learning on Temporal Graphs

We present the Temporal Graph Benchmark (TGB), a collection of challengi...

0 Shenyang Huang, et al. ∙

research

∙ 05/25/2023

From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module

Latent Graph Inference (LGI) relaxed the reliance of Graph Neural Networ...

0 Claudio Battiloro, et al. ∙

research

∙ 05/17/2023

Edge Directionality Improves Learning on Heterophilic Graphs

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

0 Emanuele Rossi, et al. ∙

research

∙ 05/13/2023

DRew: Dynamically Rewired Message Passing with Delay

Message passing neural networks (MPNNs) have been shown to suffer from t...

0 Benjamin Gutteridge, et al. ∙

research

∙ 02/06/2023

On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology

Message Passing Neural Networks (MPNNs) are instances of Graph Neural Ne...

0 Francesco Di Giovanni, et al. ∙

research

∙ 01/30/2023

Curvature Filtrations for Graph Generative Model Evaluation

Graph generative model evaluation necessitates understanding differences...

86 Joshua Southern, et al. ∙

research

∙ 10/24/2022

Structure-based Drug Design with Equivariant Diffusion Models

Structure-based drug design (SBDD) aims to design small-molecule ligands...

20 Arne Schneuing, et al. ∙

research

∙ 10/11/2022

Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design

Fragment-based drug discovery has been an effective paradigm in early-st...

5 Ilia Igashov, et al. ∙

research

∙ 10/04/2022

Hyperbolic Deep Reinforcement Learning

We propose a new class of deep reinforcement learning (RL) algorithms th...

0 Edoardo Cetin, et al. ∙

research

∙ 06/17/2022

Sheaf Neural Networks with Connection Laplacians

A Sheaf Neural Network (SNN) is a type of Graph Neural Network (GNN) tha...

109 Federico Barbero, et al. ∙

research

∙ 02/14/2022

Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization

In the sequential decision making setting, an agent aims to achieve syst...

0 Mirco Mutti, et al. ∙

research

∙ 02/02/2022

Heterogeneous manifolds for curvature-aware graph embedding

Graph embeddings, wherein the nodes of the graph are represented by poin...

13 Francesco Di Giovanni, et al. ∙

research

∙ 12/14/2021

Graph Kernel Neural Networks

The convolution operator at the core of many modern neural architectures...

194 Luca Cosmo, et al. ∙

research

∙ 11/23/2021

On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features

While Graph Neural Networks (GNNs) have recently become the de facto sta...

17 Emanuele Rossi, et al. ∙

research

∙ 09/28/2021

Unsupervised Diffeomorphic Surface Registration and Non-Linear Modelling

Registration is an essential tool in image analysis. Deep learning based...

8 Balder Croquet, et al. ∙

research

∙ 06/23/2021

Weisfeiler and Lehman Go Cellular: CW Networks

Graph Neural Networks (GNNs) are limited in their expressive power, stru...

8 Cristian Bodnar, et al. ∙

research

∙ 03/04/2021

Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks

The pairwise interaction paradigm of graph machine learning has predomin...

33 Cristian Bodnar, et al. ∙

research

∙ 11/26/2020

Non-Rigid Puzzles

Shape correspondence is a fundamental problem in computer graphics and v...

5 Or Litany, et al. ∙

research

∙ 09/10/2020

3D Facial Matching by Spiral Convolutional Metric Learning and a Biometric Fusion-Net of Demographic Properties

Face recognition is a widely accepted biometric verification tool, as th...

1 Soha Sadat Mahdi, et al. ∙

research

∙ 07/31/2020

Graph signal processing for machine learning: A review and new perspectives

The effective representation, processing, analysis, and visualization of...

46 Xiaowen Dong, et al. ∙

research

∙ 06/18/2020

Temporal Graph Networks for Deep Learning on Dynamic Graphs

Graph Neural Networks (GNNs) have recently become increasingly popular d...

11 Emanuele Rossi, et al. ∙

research

∙ 04/28/2020

Privacy-Preserving Recommender Systems Challenge on Twitter's Home Timeline

Recommender systems constitute the core engine of most social network pl...

9 Luca Belli, et al. ∙

research

∙ 04/23/2020

SIGN: Scalable Inception Graph Neural Networks

Geometric deep learning, a novel class of machine learning algorithms ex...

22 Emanuele Rossi, et al. ∙

research

∙ 04/04/2020

Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild

We introduce a simple and effective network architecture for monocular 3...

8 Dominik Kulon, et al. ∙

research

∙ 03/27/2020

Latent Patient Network Learning for Automatic Diagnosis

Recently, Graph Convolutional Networks (GCNs) has proven to be a powerfu...

0 Luca Cosmo, et al. ∙

research

∙ 02/11/2020

Differentiable Graph Module (DGM) Graph Convolutional Networks

Graph deep learning has recently emerged as a powerful ML concept allowi...

36 Anees Kazi, et al. ∙

research

∙ 01/16/2020

Graph Attentional Autoencoder for Anticancer Hyperfood Prediction

Recent research efforts have shown the possibility to discover anticance...

14 Guadalupe Gonzalez, et al. ∙

research

∙ 11/13/2019

SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator

Intrinsic graph convolution operators with differentiable kernel functio...

59 Shunwang Gong, et al. ∙

research

∙ 05/16/2019

ncRNA Classification with Graph Convolutional Networks

Non-coding RNA (ncRNA) are RNA sequences which don't code for a gene but...

21 Emanuele Rossi, et al. ∙

research

∙ 05/08/2019

Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation

Generative models for 3D geometric data arise in many important applicat...

26 Giorgos Bouritsas, et al. ∙

research

∙ 05/04/2019

Single Image 3D Hand Reconstruction with Mesh Convolutions

Monocular 3D reconstruction of deformable objects, such as human body pa...

26 Dominik Kulon, et al. ∙

research

∙ 03/25/2019

MeshGAN: Non-linear 3D Morphable Models of Faces

Generative Adversarial Networks (GANs) are currently the method of choic...

8 Shiyang Cheng, et al. ∙

research

∙ 12/01/2017

Deformable Shape Completion with Graph Convolutional Autoencoders

The availability of affordable and portable depth sensors has made scann...

0 Or Litany, et al. ∙

research

∙ 07/25/2017

Efficient Deformable Shape Correspondence via Kernel Matching

We present a method to match three dimensional shapes under non-isometri...

0 Zorah Lähner, et al. ∙

research

∙ 04/23/2015

Robust Principal Component Analysis on Graphs

Principal Component Analysis (PCA) is the most widely used tool for line...

0 Nauman Shahid, et al. ∙

research

∙ 08/07/2014

Matrix Completion on Graphs

The problem of finding the missing values of a matrix given a few of its...

0 Vassilis Kalofolias, et al. ∙

Michael Bronstein

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