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08/30/2023
RetroBridge: Modeling Retrosynthesis with Markov Bridges
Retrosynthesis planning is a fundamental challenge in chemistry which ai...
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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...
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07/03/2023
Temporal Graph Benchmark for Machine Learning on Temporal Graphs
We present the Temporal Graph Benchmark (TGB), a collection of challengi...
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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...
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05/17/2023
Edge Directionality Improves Learning on Heterophilic Graphs
Graph Neural Networks (GNNs) have become the de-facto standard tool for ...
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05/13/2023
DRew: Dynamically Rewired Message Passing with Delay
Message passing neural networks (MPNNs) have been shown to suffer from t...
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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...
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01/30/2023
Curvature Filtrations for Graph Generative Model Evaluation
Graph generative model evaluation necessitates understanding differences...
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10/24/2022
Structure-based Drug Design with Equivariant Diffusion Models
Structure-based drug design (SBDD) aims to design small-molecule ligands...
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10/11/2022
Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design
Fragment-based drug discovery has been an effective paradigm in early-st...
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10/04/2022
Hyperbolic Deep Reinforcement Learning
We propose a new class of deep reinforcement learning (RL) algorithms th...
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06/17/2022
Sheaf Neural Networks with Connection Laplacians
A Sheaf Neural Network (SNN) is a type of Graph Neural Network (GNN) tha...
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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...
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02/02/2022
Heterogeneous manifolds for curvature-aware graph embedding
Graph embeddings, wherein the nodes of the graph are represented by poin...
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12/14/2021
Graph Kernel Neural Networks
The convolution operator at the core of many modern neural architectures...
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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...
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09/28/2021
Unsupervised Diffeomorphic Surface Registration and Non-Linear Modelling
Registration is an essential tool in image analysis. Deep learning based...
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06/23/2021
Weisfeiler and Lehman Go Cellular: CW Networks
Graph Neural Networks (GNNs) are limited in their expressive power, stru...
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03/04/2021
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
The pairwise interaction paradigm of graph machine learning has predomin...
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11/26/2020
Non-Rigid Puzzles
Shape correspondence is a fundamental problem in computer graphics and v...
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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...
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07/31/2020
Graph signal processing for machine learning: A review and new perspectives
The effective representation, processing, analysis, and visualization of...
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06/18/2020
Temporal Graph Networks for Deep Learning on Dynamic Graphs
Graph Neural Networks (GNNs) have recently become increasingly popular d...
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04/28/2020
Privacy-Preserving Recommender Systems Challenge on Twitter's Home Timeline
Recommender systems constitute the core engine of most social network pl...
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04/23/2020
SIGN: Scalable Inception Graph Neural Networks
Geometric deep learning, a novel class of machine learning algorithms ex...
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04/04/2020
Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild
We introduce a simple and effective network architecture for monocular 3...
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03/27/2020
Latent Patient Network Learning for Automatic Diagnosis
Recently, Graph Convolutional Networks (GCNs) has proven to be a powerfu...
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02/11/2020
Differentiable Graph Module (DGM) Graph Convolutional Networks
Graph deep learning has recently emerged as a powerful ML concept allowi...
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01/16/2020
Graph Attentional Autoencoder for Anticancer Hyperfood Prediction
Recent research efforts have shown the possibility to discover anticance...
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11/13/2019
SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator
Intrinsic graph convolution operators with differentiable kernel functio...
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05/16/2019
ncRNA Classification with Graph Convolutional Networks
Non-coding RNA (ncRNA) are RNA sequences which don't code for a gene but...
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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...
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05/04/2019
Single Image 3D Hand Reconstruction with Mesh Convolutions
Monocular 3D reconstruction of deformable objects, such as human body pa...
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03/25/2019
MeshGAN: Non-linear 3D Morphable Models of Faces
Generative Adversarial Networks (GANs) are currently the method of choic...
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12/01/2017
Deformable Shape Completion with Graph Convolutional Autoencoders
The availability of affordable and portable depth sensors has made scann...
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07/25/2017
Efficient Deformable Shape Correspondence via Kernel Matching
We present a method to match three dimensional shapes under non-isometri...
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04/23/2015
Robust Principal Component Analysis on Graphs
Principal Component Analysis (PCA) is the most widely used tool for line...
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08/07/2014