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07/14/2023
Harpa: High-Rate Phase Association with Travel Time Neural Fields
Phase association groups seismic wave arrivals according to their origin...
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06/09/2023
A Dynamical Graph Prior for Relational Inference
Relational inference aims to identify interactions between parts of a dy...
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04/24/2023
A Transfer Principle: Universal Approximators Between Metric Spaces From Euclidean Universal Approximators
We build universal approximators of continuous maps between arbitrary Po...
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02/27/2023
Injectivity of ReLU networks: perspectives from statistical physics
When can the input of a ReLU neural network be inferred from its output?...
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01/08/2023
Deep Injective Prior for Inverse Scattering
In electromagnetic inverse scattering, we aim to reconstruct object perm...
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12/26/2022
Statistical Mechanics of Generalization In Graph Convolution Networks
Graph neural networks (GNN) have become the default machine learning mod...
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12/08/2022
Deep Variational Inverse Scattering
Inverse medium scattering solvers generally reconstruct a single solutio...
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11/18/2022
Differentiable Uncalibrated Imaging
We propose a differentiable imaging framework to address uncertainty in ...
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10/02/2022
Deep Invertible Approximation of Topologically Rich Maps between Manifolds
How can we design neural networks that allow for stable universal approx...
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09/14/2022
Small Transformers Compute Universal Metric Embeddings
We study representations of data from an arbitrary metric space 𝒳 in the...
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07/06/2022
Orthogonal Matrix Retrieval with Spatial Consensus for 3D Unknown-View Tomography
Unknown-view tomography (UVT) reconstructs a 3D density map from its 2D ...
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06/04/2022
Implicit Neural Representation for Mesh-Free Inverse Obstacle Scattering
Implicit representation of shapes as level sets of multilayer perceptron...
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04/15/2022
Conditional Injective Flows for Bayesian Imaging
Most deep learning models for computational imaging regress a single rec...
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10/08/2021
Neural Link Prediction with Walk Pooling
Graph neural networks achieve high accuracy in link prediction by jointl...
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10/08/2021
Universal Joint Approximation of Manifolds and Densities by Simple Injective Flows
We analyze neural networks composed of bijective flows and injective exp...
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10/07/2021
Universal Approximation Under Constraints is Possible with Transformers
Many practical problems need the output of a machine learning model to s...
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05/09/2021
Truly shift-equivariant convolutional neural networks with adaptive polyphase upsampling
Convolutional neural networks lack shift equivariance due to the presenc...
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02/20/2021
Trumpets: Injective Flows for Inference and Inverse Problems
We propose injective generative models called Trumpets that generalize i...
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02/01/2021
Total least squares phase retrieval
We address the phase retrieval problem with errors in the sensing vector...
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11/28/2020
Truly shift-invariant convolutional neural networks
Thanks to the use of convolution and pooling layers, convolutional neura...
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11/25/2020
Interpreting U-Nets via Task-Driven Multiscale Dictionary Learning
U-Nets have been tremendously successful in many imaging inverse problem...
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06/17/2020
Geometry of Comparisons
Many data analysis problems can be cast as distance geometry problems in...
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06/15/2020
Globally Injective ReLU Networks
We study injective ReLU neural networks. Injectivity plays an important ...
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06/10/2020
Learning the geometry of wave-based imaging
We propose a general deep learning architecture for wave-based imaging p...
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05/18/2020
Hyperbolic Distance Matrices
Hyperbolic space is a natural setting for mining and visualizing data wi...
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10/09/2019
The fastest ℓ_1,∞ prox in the west
Proximal operators are of particular interest in optimization problems d...
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07/03/2019
Don't take it lightly: Phasing optical random projections with unknown operators
In this paper we tackle the problem of recovering the phase of complex l...
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02/19/2019
Shapes from Echoes: Uniqueness from Point-to-Plane Distance Matrices
We study the problem of localizing a configuration of points and planes ...
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02/14/2019
Solving Complex Quadratic Systems with Full-Rank Random Matrices
We tackle the problem of recovering a complex signal x∈C^n from quadrat...
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01/29/2019
Learning Schatten--Von Neumann Operators
We study the learnability of a class of compact operators known as Schat...
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12/03/2018
Permutations Unlabeled beyond Sampling Unknown
A recent result on unlabeled sampling states that with probability one o...
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11/17/2018
Multipath-enabled private audio with noise
We address the problem of privately communicating audio messages to mult...
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10/18/2018
Concentration of the Frobenius norm of generalized matrix inverses
In many applications it is useful to replace the Moore-Penrose pseudoinv...
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10/18/2018
Concentration of the Frobenius norms of generalized matrix inverses
In many applications it is useful to replace the Moore-Penrose pseudoinv...
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10/18/2018
Concentration of the Frobenius norms of pseudoinverses
In many applications it is useful to replace the Moore-Penrose pseudoinv...
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09/16/2018
Cocktails, but no party: multipath-enabled private audio
We describe a private audio messaging system that uses echoes to unscram...
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05/29/2018
Deep Mesh Projectors for Inverse Problems
We develop a new learning-based approach to ill-posed inverse problems. ...
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04/06/2018
Reconstructing Point Sets from Distance Distributions
We study the problem of reconstructing the locations u_i of a set of po...
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01/11/2018
Direction of Arrival with One Microphone, a few LEGOs, and Non-Negative Matrix Factorization
Conventional approaches to sound source localization require at least tw...
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11/18/2017
Separake: Source Separation with a Little Help From Echoes
It is commonly believed that multipath hurts various audio processing al...
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10/11/2017