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Learning disentangled representations via product manifold projection
We propose a novel approach to disentangle the generative factors of variation underlying a given set of observations. Our method …
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Learning disentangled representations via product manifold projection
Shape registration in the time of transformers
In this paper, we propose a transformer-based procedure for the efficient registration of non-rigid 3D point clouds. The proposed …
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Shape registration in the time of transformers
Cluster-driven Graph Federated Learning over Multiple Domains
Federated Learning (FL) deals with learning a central model (i.e. the server) in privacy-constrained scenarios, where data are stored …
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Cluster-driven Graph Federated Learning over Multiple Domains
Fast Sinkhorn Filters: Using Matrix Scaling for Non-Rigid Shape Correspondence With Functional Maps
In this paper, we provide a theoretical foundation for pointwise map recovery from functional maps and highlight its relation to a …
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Fast Sinkhorn Filters: Using Matrix Scaling for Non-Rigid Shape Correspondence With Functional Maps
Universal Spectral Adversarial Attacks for Deformable Shapes
Machine learning models are known to be vulnerable to adversarial attacks, namely perturbations of the data that lead to wrong …
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Universal Spectral Adversarial Attacks for Deformable Shapes
Efficiently parallelizable strassen-based multiplication of a matrix by its transpose
An efficient algorithm for computing the multiplication of a matrix by its transpose, exploiting Strassen-like recursions. The algorithm is designed to be parallelized with multiple paradigms, from multi-threading to distributed computing.
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Efficiently parallelizable strassen-based multiplication of a matrix by its transpose
Intra-operative Update of Boundary Conditions for Patient-Specific Surgical Simulation
Patient-specific Biomechanical Models (PBMs) can enhance computer assisted surgical procedures with critical information. Although …
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Intra-operative Update of Boundary Conditions for Patient-Specific Surgical Simulation
Orthogonalized Fourier polynomials for signal approximation and transfer
The usual Laplacian eigenbasis is extended to consider also polynomials of the eigenfunctions. The new extended basis has in increased descriptive power in signal reconstruction and transfer tasks, coming at a very reduced cost.
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Orthogonalized Fourier polynomials for signal approximation and transfer
Instant recovery of shape from spectrum via latent space connections
We introduce the first learning-based method for recovering shapes from Laplacian spectra. Given an auto-encoder, our model takes the …
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Instant recovery of shape from spectrum via latent space connections
Towards Precise Completion of Deformable Shapes
According to Aristotle, the whole is greater than the sum of its parts. This statement was adopted to explain human perception by the …
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Towards Precise Completion of Deformable Shapes