The paper reconstructs Lorentzian spacetimes from causal sets.
problem Reconstructing Lorentzian spacetimes from causal sets.
method Introduced a concept of isomorphy and three types of convergence.
result Established Gromov's reconstruction theorem in Lorentzian geometry.
Describes reconstructing Poisson structures from Lie group actions.
problem Reconstructing invariant Poisson structures from Lie group actions.
method Describes reconstruction of invariant Poisson structures from canonical actions of compact Lie groups on fibered phase spaces.
result Derives symmetry properties of Wong's type equations from main results.
Algorithm reconstructs conserved networks from flow data.
problem Network reconstruction from flow data.
method Polynomial time algorithm exploiting graph theoretic properties and learning techniques.
result Exact network reconstruction possible for arborescence networks.
This work analyzes the generalization properties of learned reconstruction methods for inverse problems.
problem Understanding the reliability and stability of learned reconstruction methods for inverse problems.
method Develops a general framework to interpret learned reconstruction methods in statistical learning context and performs their sample error analysis.
result Estimates the dependence of learned operators on training data, providing insights into their generalization properties.
IAGAN method improves medical image reconstruction by incorporating adaptive GAN priors.
problem Reconstructing high-fidelity medical images from incomplete data.
method Image-adaptive GAN-based reconstruction method (IAGAN).
result IAGAN can recover fine structures relevant for medical diagnosis.
Paper addresses data reconstruction from privacy-protected templates using STCA.
problem Reconstructing privacy-sensitive data from protected templates.
method Sparse ternary coding with ambiguization (STCA) for privacy preservation.
result STCA maintains theoretical performance against deep reconstruction attacks for synthetic data but requires special measures for real images.
SUPER learning combines supervised and unsupervised methods for LDCT image reconstruction.
problem Low-dose CT image reconstruction challenges.
method Combines supervised and unsupervised learning methods.
result SUPER learning dramatically outperforms constituent methods.
Develops Active Fourier Auditor to estimate ML model properties without reconstructing them.
problem Verifying and auditing properties of Machine Learning models in real-world applications.
method A new framework that quantifies ML model properties using Fourier coefficients, without reconstructing the model.
result Active Fourier Auditor (AFA) is more accurate and sample-efficient than baselines for estimating robustness, individual fairness, and group fairness.
Reconstruction based subspace clustering methods compute a self reconstruction matrix over the samples and use it for spectral clustering to obtain the final clustering result. Their success largely relies on the assumption that the underlying subspaces are independent, which, however, does not always hold in the appli…
A major problem in the study of complex socioeconomic systems is represented by privacy issues−that can put severe limitations on the amount of accessible information, forcing to build models on the basis of incomplete knowledge. In this paper we investigate a novel method to reconstruct global topological properties…
CNNs reconstruct medium properties from wave probing responses.
problem Determining medium properties from wave responses.
method Deep convolutional neural networks (CNNs) for nonlinear wave equations.
result Quantitative dependence of network depth and units on medium complexity.
Review of network reconstruction methods for economic and financial systems.
problem Limited data on financial institutions' interconnections.
method Systematic comparison of network reconstruction methods.
result Lack of a unified framework for network reconstruction methods.
Two-layer model sparsifies image residuals for CT image reconstruction.
problem Image reconstruction from limited and corrupted data.
method Pre-learning a two-layer sparsifying transform model with block coordinate descent optimization.
result Preliminary experiments show the two-layer model improves CT image reconstruction from low-dose measurements.
New method uses generative models to improve phase retrieval stability.
problem Improving stability of solutions in phase retrieval problems.
method Unified reconstruction approach using generative models to mitigate overfitting.
result Mitigates overfitting to generative model for varying noise levels.
New method reconstructs network topology from node-dynamics data.
problem Reconstructing network topology from time-resolved observations of node-dynamics.
method Feature ranking using Random forest and RReliefF to rank node importance.
result Method is robust to various system parameters and depends on dynamical regime.
Paper studies Frank-Wolfe algorithm for solving sparse reconstruction problems.
problem Sparse reconstruction problem
method Frank-Wolfe algorithm applied to quasi-incoherent dictionaries
result Algorithm converges exponentially fast for quasi-incoherent dictionaries
BCD-Net improves low-dose CT image reconstruction.
problem Challenges in obtaining accurate low-dose CT images.
method Modified iterative regression CNN, BCD-Net, with faster numerical solvers.
result BCD-Net achieves better image quality and generalization than state-of-the-art methods.
Improves deep network generalization for image sequence reconstruction.
problem Improving generalization of deep networks for inverse image reconstruction.
method Proposes a network optimized by a variational approximation of the information bottleneck principle with stochastic latent space.
result Demonstrates improved generalization ability of inverse reconstruction networks through stochasticity and information bottleneck.
Generative adversarial networks reconstruct oolitic limestone micro-structures.
problem Stochastic image reconstruction of oolitic limestone micro-structures.
method Generative adversarial neural networks (GANs) for unsupervised learning.
result GANs accurately reconstruct oolitic limestone micro-structures.
This paper studies CSDL's reconstruction risk and finds it consistent in the ultra-sparse setting.
problem The statistical properties of convolutional sparse dictionary learning (CSDL).
method Identifies the minimax convergence rate of CSDL in terms of reconstruction risk, upper bounds the risk of an established CSDL estimator, and proves a matching lower bound.
result Consistency in reconstruction risk is possible precisely in the ultra-sparse setting.
New model reconstructs networks by identifying regular components.
problem Uncovering the complexity of network structures.
method Low-rank pursuit based self-representation network model.
result Reconstructs networks and measures their regulability.
New analysis shows reconstruction attacks are unreliable without prior data knowledge.
problem Privacy and security risks from neural network memorization of training data.
method Complementary analysis of reconstruction methods, proving their unreliability without prior data knowledge.
result Reconstruction attacks are fundamentally unreliable without prior data knowledge, and networks trained more extensively are less susceptible.
Self-supervised method learns from unlabelled point clouds by reconstructing them.
problem Efficiently learning from large, unlabelled 3D point cloud datasets.
method Trains neural networks to reconstruct point clouds with randomly rearranged parts.
result Method learns semantic properties of point clouds and improves downstream object classification.
A new method improves molecule generation accuracy and efficiency.
problem Posterior collapse in VAEs for molecule sequence generation.
method Re-balancing reconstruction loss to avoid posterior collapse.
result Our method achieves state-of-the-art reconstruction accuracy and competitive validity.
Deep learning model reconstructs material microstructures from feature representations.
problem Reconstructing complex material microstructures accurately and efficiently.
method Convolutional deep belief network for automated feature learning and dimension reduction.
result Material reconstructions preserve microstructural features and material properties.
New method samples from posterior distributions of network reconstructions.
problem Inferring unseen interactions in systems from observed dynamics.
method Efficient MCMC algorithm for sampling posterior distributions.
result Reveals full population of answers weighted by plausibility.
A VAE model predicts material properties and microstructures.
problem Building forward and inverse structure-property linkages in materials science.
method Combines VAE with regression, using a two-level prior and multi-modal Gaussian mixture.
result The model achieves accurate forward and inverse predictions of material properties and microstructures.
WaveNet reconstructs speech from brain activity, revealing acoustic features.
problem Reconstructing speech from brain activity with limited data.
method WaveNet model applied to STG intracranial recordings.
result WaveNet models reveal phoneme-level acoustic features.
Jointly estimates flow fields and particle properties from Lagrangian data.
problem Estimating flow fields and particle properties from sparse, noisy Lagrangian data.
method Data assimilation framework coupling Eulerian and Lagrangian models.
result Joint estimation of flow fields and particle properties in various flow regimes.
Paper improves deep learning models for cardiac potential reconstruction.
problem Improving generalization of sequence models for cardiac potential reconstruction.
method Constrained stochasticity and global aggregation of temporal information in latent space.
result Improved generalization of inverse reconstruction networks.
A new method reduces dimensionality for better likelihood-free parameter estimation.
problem Estimating parameters from data with no closed-form likelihood.
method Combines reconstruction map estimation with dimension-reduction techniques.
result The proposed method outperforms existing techniques in accuracy and efficiency.
This paper tackles disentanglement in image editing and reconstruction.
problem Learning disentangled image representations and balancing disentanglement strength and reconstruction quality.
method Distance covariance based decorrelation regularization for disentanglement, soft target representation for reconstruction, and collapsing AE decoder and GAN generator.
result The proposed model improves the disentanglement strength and perceptual quality of generated images.
Method preserves Hamiltonian structure for unknown systems from noisy data.
problem Reconstructing unknown Hamiltonian systems from trajectory data.
method Directly approximates the unknown Hamiltonian, enforcing conservation.
result Structure-preserving property demonstrated and effective in numerical examples.
New method controls posterior collapse in VAEs without network architecture constraints.
problem Posterior collapse in VAEs reduces diversity of generated samples.
method Introduces Latent Reconstruction (LR) loss to control posterior collapse.
result Controls posterior collapse on various datasets without architectural constraints.
Regularizes 3D inverse scattering with tangent-point energy for better solutions.
problem Ill-conditioned inverse obstacle scattering problems in 3D.
method Tikhonov regularization using tangent-point energy to penalize surface roughness and ensure well-posedness.
result Regularized solutions converge to true solution as noise level decreases.
Paper proposes a new model for robust feature extraction.
problem Developing a robust method for feature representation.
method Double Denoising Auto-Encoders (DDAEs) model, using corruption and reconstruction on both input and hidden representation.
result The proposed model achieves better robustness and feature learning than state-of-the-art models.
Novel method uses U-net for seismic data reconstruction without large datasets.
problem Reconstruction of seismic data with missing traces.
method Unsupervised learning with U-net exploiting deep seismic prior.
result DSPRecon algorithm outperforms SSA and Cadzow methods in reconstruction performance.
Proposes a new sparse recovery method using generalized error function.
problem Sparse recovery in signal processing and imaging.
method Introduces a penalty function with shape and scale parameters for sparse recovery.
result The method improves MRI reconstruction and is theoretically sound.
The article recovers tensor fields from partial data using weighted divergent ray transforms.
problem Recovering tensor fields from partial data.
method Weighted divergent ray transforms, unique continuation property of fractional Laplacian, explicit reconstruction formulas.
result Recovery of symmetric m-tensor fields and unique continuation for vector fields and symmetric 2-tensor fields. Paper reconstructs compact Riemannian manifolds from travel time data.
problem Reconstructing compact Riemannian manifolds from partial travel time data.
method Embedding in function space, studying distance function regularity.
result Reconstruction of compact Riemannian manifolds from travel time data.
New quantum state reconstruction method accelerates convergence.
problem Quantum state reconstruction for larger systems.
method Momentum-Inspired Factored Gradient Descent (MiFGD) combining compressed sensing, non-convex optimization, and acceleration.
result Converges to true density matrix at an accelerated linear rate, provably close to the true matrix.
Column normalization doesn't ensure good sparse recovery for random matrices.
problem Ensuring good sparse recovery properties for column-normalized random matrices.
method Constructing a random vector and showing that column normalization doesn't lead to desired recovery properties.
result Column-normalized random matrices do not satisfy exact reconstruction property with high probability.
Medical image reconstruction advances from sparse models to machine learning.
problem Improving image quality and reducing noise in medical imaging.
method Iterative reconstruction, modified data acquisition methods, and machine learning models.
result Machine learning methods show promise in improving image quality.
The paper explores Parseval frames on vector bundles, proving their existence for certain cases.
problem Existence of Parseval frames on vector bundles.
method Using G-bundles and algebraic topology, the authors prove the existence of Parseval frames for orientable vector bundles and provide conditions for smaller size frames. result The existence of Parseval frames for orientable vector bundles and conditions for smaller size frames.
Shape manifold and elastic energy regularization help reconstruct complex obstacles from scattering data.
problem Reconstructing non-star-shaped obstacles from scattered waves.
method Shape manifold, Tikhonov regularization, Möbius energy penalization.
result The approach yields stable and accurate reconstructions of complex obstacles.
Total variation denoising improves image quality adaptively.
problem Improving image quality from noisy data.
method Total variation regularization for image denoising.
result Denoised images converge to true images at a parametric rate.
New results on inferring hidden states in trackable weak models.
problem Inferring hidden states in trackable weak models.
method Analyzing strongly-connected trackable weak models and reconstructing branch choices.
result The number of hypotheses in strongly-connected trackable models is bounded by a constant.
The paper recovers contact forms from boundary data using vector fields and Lyapunov functions.
problem Recovering contact forms from boundary data.
method Using vector fields and Lyapunov functions, the paper describes boundary data and proves reconstruction of (X,β) up to diffeomorphism. result Boundary data allow for the reconstruction of (X,β) up to a diffeomorphism of X.