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06/10/2023
Neural Injective Functions for Multisets, Measures and Graphs via a Finite Witness Theorem
Injective multiset functions have a key role in the theoretical study of...
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01/31/2023
Complete Neural Networks for Euclidean Graphs
We propose a 2-WL-like geometric graph isomorphism test and prove it is ...
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07/18/2022
Symmetrized Robust Procrustes: Constant-Factor Approximation and Exact Recovery
The classical Procrustes problem is to find a rigid motion (orthogonal t...
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05/05/2022
Low Dimensional Invariant Embeddings for Universal Geometric Learning
This paper studies separating invariants: mappings on d-dimensional semi...
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03/02/2022
A Simple and Universal Rotation Equivariant Point-cloud Network
Equivariance to permutations and rigid motions is an important inductive...
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07/28/2021
Neural Network Approximation of Refinable Functions
In the desire to quantify the success of neural networks in deep learnin...
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10/06/2020
On the Universality of Rotation Equivariant Point Cloud Networks
Learning functions on point clouds has applications in many fields, incl...
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06/21/2020
Stable Phase Retrieval from Locally Stable and Conditionally Connected Measurements
This paper is concerned with stable phase retrieval for a family of phas...
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05/27/2019
Expression of Fractals Through Neural Network Functions
To help understand the underlying mechanisms of neural networks (NNs), s...
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04/03/2019
Linearly Converging Quasi Branch and Bound Algorithms for Global Rigid Registration
In recent years, several branch-and-bound (BnB) algorithms have been pro...
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11/06/2017
A Linear Variational Principle for Riemann Mappings and Discrete Conformality
We consider Riemann mappings from bounded Lipschitz domains in the plane...
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11/06/2017
A Linear Variational Principle for Riemann Mapping and Discrete Conformality
We consider Riemann mappings from bounded Lipschitz domains in the plane...
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05/17/2017
