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Found 10 papers, 4 papers with code
Mixture of Experts Soften the Curse of Dimensionality in Operator Learning
In this paper, we construct a mixture of neural operators (MoNOs) between function spaces whose complexity is distributed over a network of expert neural operators (NOs), with each NO satisfying parameter scaling restrictions.
Beyond Hawkes: Neural Multi-event Forecasting on Spatio-temporal Point Processes
Predicting discrete events in time and space has many scientific applications, such as predicting hazardous earthquakes and outbreaks of infectious diseases.
Deep Invertible Approximation of Topologically Rich Maps between Manifolds
By exploiting the topological parallels between locally bilipschitz maps, covering spaces, and local homeomorphisms, and by using universal approximation arguments from machine learning, we find that a novel network of the form $\mathcal{T} \circ p \circ \mathcal{E}$, where $\mathcal{E}$ is an injective network, $p$ a fixed coordinate projection, and $\mathcal{T}$ a bijective network, is a universal approximator of local diffeomorphisms between compact smooth submanifolds embedded in $\mathbb{R}^n$.
Conditional Injective Flows for Bayesian Imaging
Most deep learning models for computational imaging regress a single reconstructed image.
Universal Joint Approximation of Manifolds and Densities by Simple Injective Flows
We show that in general, injective flows between $\mathbb{R}^n$ and $\mathbb{R}^m$ universally approximate measures supported on images of extendable embeddings, which are a subset of standard embeddings: when the embedding dimension m is small, topological obstructions may preclude certain manifolds as admissible targets.
Trumpets: Injective Flows for Inference and Inverse Problems
We propose injective generative models called Trumpets that generalize invertible normalizing flows.
Globally Injective ReLU Networks
Injectivity plays an important role in generative models where it enables inference; in inverse problems and compressed sensing with generative priors it is a precursor to well posedness.
Learning the geometry of wave-based imaging
We propose a general physics-based deep learning architecture for wave-based imaging problems.
Learning Schatten--von Neumann Operators
We study the learnability of a class of compact operators known as Schatten--von Neumann operators.
Inverse Problems with Invariant Multiscale Statistics
We propose a new approach to linear ill-posed inverse problems.
Computational Engineering, Finance, and Science
