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Found 19 papers, 6 papers with code
Nonparametric Estimation via Variance-Reduced Sketching
In this paper, we introduce a new framework called Variance-Reduced Sketching (VRS), specifically designed to estimate density functions and nonparametric regression functions in higher dimensions with a reduced curse of dimensionality.
Matching via Distance Profiles
In this paper, we introduce and study matching methods based on distance profiles.
Combining Monte Carlo and Tensor-network Methods for Partial Differential Equations via Sketching
In this paper, we propose a general framework for solving high-dimensional partial differential equations with tensor networks.
Tensorizing flows: a tool for variational inference
Fueled by the expressive power of deep neural networks, normalizing flows have achieved spectacular success in generative modeling, or learning to draw new samples from a distribution given a finite dataset of training samples.
Deep Neural-network Prior for Orbit Recovery from Method of Moments
In the multireference alignment case, we demonstrate the advantage of using the NN to accelerate the convergence for the reconstruction of signals from the moments.
Generative Modeling via Hierarchical Tensor Sketching
We propose a hierarchical tensor-network approach for approximating high-dimensional probability density via empirical distribution.
High-dimensional density estimation with tensorizing flow
We propose the tensorizing flow method for estimating high-dimensional probability density functions from the observed data.
Generative Modeling via Tree Tensor Network States
In this paper, we present a density estimation framework based on tree tensor-network states.
Reinforced Inverse Scattering
Inverse wave scattering aims at determining the properties of an object using data on how the object scatters incoming waves.
A Spectral Method for Joint Community Detection and Orthogonal Group Synchronization
Community detection and orthogonal group synchronization are both fundamental problems with a variety of important applications in science and engineering.
Joint Community Detection and Rotational Synchronization via Semidefinite Programming
In the presence of heterogeneous data, where randomly rotated objects fall into multiple underlying categories, it is challenging to simultaneously classify them into clusters and synchronize them based on pairwise relations.
A semigroup method for high dimensional committor functions based on neural network
This paper proposes a new method based on neural networks for computing the high-dimensional committor functions that satisfy Fokker-Planck equations.
NMR Assignment through Linear Programming
Nuclear Magnetic Resonance (NMR) Spectroscopy is the second most used technique (after X-ray crystallography) for structural determination of proteins.
Drop-Activation: Implicit Parameter Reduction and Harmonic Regularization
Overfitting frequently occurs in deep learning.
Ranked #10 on
Image Classification
on SVHN
Solving for high dimensional committor functions using artificial neural networks
In this note we propose a method based on artificial neural network to study the transition between states governed by stochastic processes.
Solving parametric PDE problems with artificial neural networks
The representability of such quantity using a neural-network can be justified by viewing the neural-network as performing time evolution to find the solutions to the PDE.
Numerical Analysis 65Nxx
Non-iterative rigid 2D/3D point-set registration using semidefinite programming
We describe a convex programming framework for pose estimation in 2D/3D point-set registration with unknown point correspondences.
Open problem: Tightness of maximum likelihood semidefinite relaxations
We have observed an interesting, yet unexplained, phenomenon: Semidefinite programming (SDP) based relaxations of maximum likelihood estimators (MLE) tend to be tight in recovery problems with noisy data, even when MLE cannot exactly recover the ground truth.
Global registration of multiple point clouds using semidefinite programming
We empirically demonstrate that (a) unlike the spectral relaxation, the relaxation gap is mostly zero for the semidefinite program (i. e., we are able to solve the original non-convex least-squares problem) up to a certain noise threshold, and (b) the semidefinite program performs significantly better than spectral and manifold-optimization methods, particularly at large noise levels.
