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Utilising Graph Machine Learning within Drug Discovery and Development
Graph Machine Learning (GML) is receiving growing interest within the ph...
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Tuning Word2vec for Large Scale Recommendation Systems
Word2vec is a powerful machine learning tool that emerged from Natural L...
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Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting
While Graph Neural Networks (GNNs) have achieved remarkable results in a...
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Learning interpretable disease self-representations for drug repositioning
Drug repositioning is an attractive cost-efficient strategy for the deve...
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Transferability of Spectral Graph Convolutional Neural Networks
This paper focuses on spectral graph convolutional neural networks (Conv...
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Fake News Detection on Social Media using Geometric Deep Learning
Social media are nowadays one of the main news sources for millions of p...
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Isospectralization, or how to hear shape, style, and correspondence
The question whether one can recover the shape of a geometric object fro...
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Functional Maps Representation on Product Manifolds
We consider the tasks of representing, analyzing and manipulating maps b...
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Nonisometric Surface Registration via Conformal Laplace-Beltrami Basis Pursuit
Surface registration is one of the most fundamental problems in geometry...
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Graph Neural Networks for IceCube Signal Classification
Tasks involving the analysis of geometric (graph- and manifold-structure...
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Dual-Primal Graph Convolutional Networks
In recent years, there has been a surge of interest in developing deep l...
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PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
Deep learning systems have become ubiquitous in many aspects of our live...
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MotifNet: a motif-based Graph Convolutional Network for directed graphs
Deep learning on graphs and in particular, graph convolutional neural ne...
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Dynamic Graph CNN for Learning on Point Clouds
Point clouds provide a flexible and scalable geometric representation su...
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Subspace Least Squares Multidimensional Scaling
Multidimensional Scaling (MDS) is one of the most popular methods for di...
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Localized Manifold Harmonics for Spectral Shape Analysis
The use of Laplacian eigenfunctions is ubiquitous in a wide range of com...
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Generative Convolutional Networks for Latent Fingerprint Reconstruction
Performance of fingerprint recognition depends heavily on the extraction...
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Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Matrix completion models are among the most common formulations of recom...
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Geometric deep learning on graphs and manifolds using mixture model CNNs
Deep learning has achieved a remarkable performance breakthrough in seve...
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Geometric deep learning: going beyond Euclidean data
Many scientific fields study data with an underlying structure that is a...
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Learning shape correspondence with anisotropic convolutional neural networks
Establishing correspondence between shapes is a fundamental problem in g...
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Efficient Globally Optimal 2D-to-3D Deformable Shape Matching
We propose the first algorithm for non-rigid 2D-to-3D shape matching, wh...
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Partial Functional Correspondence
In this paper, we propose a method for computing partial functional corr...
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Geodesic convolutional neural networks on Riemannian manifolds
Feature descriptors play a crucial role in a wide range of geometry anal...
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Functional correspondence by matrix completion
In this paper, we consider the problem of finding dense intrinsic corres...
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Shape-from-intrinsic operator
Shape-from-X is an important class of problems in the fields of geometry...
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Sparse similarity-preserving hashing
In recent years, a lot of attention has been devoted to efficient neares...
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Heat kernel coupling for multiple graph analysis
In this paper, we introduce heat kernel coupling (HKC) as a method of co...
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Structure-preserving color transformations using Laplacian commutativity
Mappings between color spaces are ubiquitous in image processing problem...
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Making Laplacians commute
In this paper, we construct multimodal spectral geometry by finding a pa...
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Multimodal diffusion geometry by joint diagonalization of Laplacians
We construct an extension of diffusion geometry to multiple modalities t...
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Multimodal similarity-preserving hashing
We introduce an efficient computational framework for hashing data belon...
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Descriptor learning for omnidirectional image matching
Feature matching in omnidirectional vision systems is a challenging prob...
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Multimodal diff-hash
Many applications require comparing multimodal data with different struc...
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Kernel diff-hash
This paper presents a kernel formulation of the recently introduced diff...
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A correspondence-less approach to matching of deformable shapes
Finding a match between partially available deformable shapes is a chall...
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Diffusion framework for geometric and photometric data fusion in non-rigid shape analysis
In this paper, we explore the use of the diffusion geometry framework fo...
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Affine-invariant geodesic geometry of deformable 3D shapes
Natural objects can be subject to various transformations yet still pres...
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Affine-invariant diffusion geometry for the analysis of deformable 3D shapes
We introduce an (equi-)affine invariant diffusion geometry by which surf...
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Diffusion-geometric maximally stable component detection in deformable shapes
Maximally stable component detection is a very popular method for featur...
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The Video Genome
Fast evolution of Internet technologies has led to an explosive growth o...
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