New condition for reconstructing Morse functions on 3D manifolds.
problem Reconstructing Morse functions with specific level sets.
method Studied a necessary and sufficient condition for reconstruction.
result New condition strengthens previous sufficient conditions.
Study reconstructs Morse-Bott functions with specific preimage conditions on 3D manifolds.
problem Reconstructing Morse-Bott functions with prescribed preimages on 3D manifolds.
method Conditions and approach based on previous work by Sharko and others.
result New result on reconstruction of nice smooth functions with specified preimages.
New benchmark for EEG-eye movement reconstruction from functional data.
problem Reconstructing eye movements from EEG data.
method Functional neural networks and open challenges for evaluation.
result Baseline results for consumer-grade and research-grade hardware.
A new loss function for set reconstruction without order consideration.
problem Reconstructing sets of elements without considering their order.
method Set Cross Entropy, a permutation-invariant loss function.
result Natural information-theoretic interpretation and successful evaluations in tasks.
Method reconstructs networks and identifies communities from dynamic data.
problem Reconstructing networks and identifying communities from dynamic data.
method Nonparametric Bayesian approach that simultaneously infers network structure and community membership.
result Joint reconstruction and community detection improve each other's accuracy.
DynAE improves deep clustering by dynamically shifting from reconstruction to centroid construction.
problem Lack of clear cost functions in unsupervised learning for capturing variations and similarities.
method Dynamic Autoencoder (DynAE) that gradually eliminates reconstruction in favor of centroid construction.
result DynAE achieves state-of-the-art results in deep clustering compared to other methods.
Paper proposes a new loss function to improve image reconstruction quality.
problem Blurred images when using pixel loss for convolutional autoencoders.
method Introduces spatial frequency loss (SFL) to mitigate blurring.
result Reduced blurs in reconstructed images using SFL.
Method calculates Shapley values for PCA reconstruction errors to explain anomaly detection.
problem Explaining PCA-based anomaly detection results.
method Utilizes probabilistic PCA view to compute Shapley values of reconstruction errors.
result Shapley values are more advantageous than raw errors for explaining anomalies.
Reconstructs piecewise constant functions from geodesic integrals.
problem Recovering piecewise constant functions from X-ray data.
method Injectivity proof using variations through geodesics, improved for simple manifolds.
result Explicit formulas for function values near the boundary and stability analysis.
Paper proposes an algorithm to reconstruct optimal model structure from graph adjacency matrix.
problem Optimal model structure reconstruction from weighted colored graph adjacency matrix.
method Uses prize-collecting Steiner tree algorithm to reconstruct minimum spanning tree.
result Demonstrates the effectiveness of the prize-collecting Steiner tree algorithm for model structure reconstruction.
End-to-end speech separation with improved phase reconstruction.
problem Cocktail party problem: separating multiple speakers in a single-channel recording.
method End-to-end approach using deep learning with unfolded phase reconstruction iterations and novel activation functions.
result State-of-the-art performance on SI-SDR and SDR metrics.
Efficiently reconstructs jump-diffusion processes from data using neural networks.
problem Reconstructing jump-diffusion processes from data.
method Temporally decoupled squared Wasserstein distance method using parameterized neural networks.
result Enhanced reconstruction of jump-diffusion processes from data.
Survey of deep learning methods for fMRI natural image reconstruction.
problem Reconstructing natural images from fMRI brain activity.
method Survey of deep learning approaches, including architectural design, datasets, and evaluation metrics.
result Performance evaluation across standardized metrics.
ENSURE framework trains deep image recon algorithms without clean data.
problem Lack of clean, fully sampled ground-truth data for deep learning image reconstruction.
method Introduces ENSURE framework, a generalization of SURE and GSURE to random sampling patterns.
result ENSURE loss function is an unbiased estimate for true mean-square error.
Bayesian method improves parameter reconstruction from many measurements.
problem Efficiently reconstructing parameters from many experimental measurements.
method Bayesian target-vector optimization considering all model outputs.
result Outperforms established optimization methods in accuracy and efficiency.
Given a Morse 2-function f:X4→S2, we give minimal conditions on the fold curves and fibers so that X4 and f can be reconstructed from a certain combinatorial diagram attached to S2. Additional remarks are made in other dimensions.
Generative networks improve fluid simulation quality by focusing on high frequencies.
problem Low-frequency details missing in fluid simulation reconstructions.
method Frequency-aware loss function for generative networks.
result Improved perceptual quality of fluid simulation results in mid-frequency bands.
RODEO speeds up MRI and CT image reconstruction from sparse data.
problem Real-time reconstruction of dynamic medical images from limited data.
method Autoencoder with robust l1-norm cost function and Split Bregman method.
result Real-time image reconstruction with minimal quality loss.
Reconstructing polytopes with fixed facet directions from support function evaluations.
problem Reconstructing polytopes with known facet directions from limited data.
method Least-squares estimate via convex quadratic program, combinatorial characterization for uniqueness, algorithm convergence.
result The least-squares estimate for a fixed simplicial normal fan is a convex quadratic program, and the solution is unique under certain conditions.
A new interpolation-based method for nonparametric regression.
problem Nonparametric regression challenges in computational complexity and experimental design.
method Reconstruction approach using interpolators and regularized least squares.
result Effective surrogates for complex methods with reduced computational burden.
MFSSA improves reconstruction accuracy of multivariate functional time series.
problem Improving reconstruction accuracy of multivariate functional time series.
method Developed MFSSA, a functional extension of MSSA, for different dimensional domains.
result Better reconstruction accuracy of MFTS signals using MFSSA compared to other methods.
Improves MRI-based brain surface reconstruction with minimal deformation energy loss.
problem Ensuring optimal deformation energy and consistency in learning-based cortical surface reconstruction.
method Design and implementation of a Minimal Energy Deformation (MED) loss in the V2C-Flow model.
result Significant improvements in training consistency and reproducibility without sacrificing reconstruction accuracy and topological correctness.
Paper optimizes objective function for inverse Ising problem.
problem Reconstructing Ising model parameters from spin configurations.
method Convex optimization to maximize an objective function.
result Optimal objective function outperforms existing methods.
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.
Researchers reconstruct algebraic maps onto curves based on prescribed Reeb graphs.
problem Reconstructing smooth real algebraic maps onto curves with specific Reeb graphs.
method Developed a method to reconstruct functions from general finite graphs, focusing on curves.
result Reconstructed functions from prescribed Reeb graphs, providing a new approach in real algebraic geometry.
Reconstruction of density functions and their characteristic functions by radial basis functions with scattered data points is a popular topic in the theory of pricing of basket options. Such functions are usually entire or admit an analytic extension into an appropriate tube and "bell-shaped" with rapidly decaying tai…
RBM models reveal how hidden unit tail behavior affects pattern reconstruction.
problem Understanding how the tail behavior of hidden units in RBMs influences pattern reconstruction.
method Identified an effective energy function for RBMs and studied its local minima.
result The ability to reconstruct patterns depends on the tail behavior of the hidden unit prior distribution.
Study shows how numerical discretization affects reconstructions and parameter distributions in nano metrology.
problem Impact of numerical discretization on parameter reconstructions and model parameter distributions.
method Bayesian target vector optimization, finite element model, Gaussian process, stochastic machine learning surrogate models, Markov chain Monte Carlo sampler.
result Numerical discretization parameters impact the accuracy and distribution of reconstructed model parameters.
The Funk-Minkowski transform and spherical convolution reconstruct functions and vector fields on the sphere.
problem Reconstructing functions and vector fields on the sphere using Funk-Minkowski transform and Hilbert type spherical convolution.
method Inversion formula for Funk-Minkowski transform and Helmholtz-Hodge decomposition solution using spherical convolution.
result Complete reconstruction of functions and vector fields on the sphere.
Study examines stability of image-reconstruction algorithms using variational regularization.
problem Stability and robustness of image-reconstruction algorithms in medical imaging.
method Review and novel stability results for ℓp-regularized linear inverse problems, focusing on p∈(1,∞). result Guarantees Lipschitz continuity for small p and Hölder continuity for larger p in Lp(Ω) function spaces. New CT image reconstruction method reduces X-ray dose while improving image quality.
problem Reducing X-ray dose in CT while maintaining image quality.
method Combines PWLS with learned sparsifying transform using alternating optimization and relaxed OS-LALM.
result Proposed method improves image quality for low dose levels compared to existing methods.
Enhances graph function reconstruction for dynamic graphs and time-varying functions.
problem Reconstructing attributes of vertices at different time instants on evolving graphs.
method Kernel-based approach for spatiotemporal dynamics, accommodating time-evolving topologies.
result Improved flexibility and computational efficiency compared to existing methods.
The paper extends a neural model to estimate uncertainty in multi-interaction trajectory reconstruction.
problem Lack of uncertainty estimation in neural models for multi-interaction trajectory reconstruction.
method Extended Factorised Neural Relational Inference model to output mean and standard deviation for each component of the phase space vector, using various loss functions.
result Demonstrated the importance of physical meaning of variables and existence of local minima during training.
New method encodes 3D object geometry into neural network weights for efficient reconstruction.
problem Efficiently representing and reconstructing 3D objects with minimal parameters.
method Mapping network that encodes object geometry into neural network weights, reconstructing objects using simple geometric spaces.
result Reconstructed objects have accuracy comparable to state-of-the-art methods with significantly fewer parameters.
A novel likelihood function for MRFs approximates marginal likelihoods and uses copulas to reconstruct the joint likelihood.
problem Intractable partition function for MRF likelihoods.
method Approximate marginal likelihoods through a modified coin-tossing scenario, then reconstruct the joint likelihood using copulas.
result Our approach outperforms Laplace approximation and pseudolikelihood, especially as MRF size increases.
SANs use sparse activation functions to compress data representations.
problem Learning meaningful features without considering compression.
method Introduce φ metric, define activation functions, and present SANs.
result SANs achieve small description length and interpretable kernels.
A new method for the unsupervised learning of sparse representations using autoencoders is proposed and implemented by ordering the output of the hidden units by their activation value and progressively reconstructing the input in this order. This can be done efficiently in parallel with the use of cumulative sums and …
New method improves model reconstruction using counterfactuals and polytope theory.
problem Reconstructing models with minimal input changes and avoiding decision boundary shifts.
method Using polytope theory to derive loss functions that treat counterfactuals differently from ordinary instances.
result Improves fidelity between target and surrogate model predictions on multiple datasets.
Paper reconstructs training data from a single gradient query.
problem Privacy threats in federated learning due to model gradients.
method Provable attack using tensor decomposition.
result Training samples can be fully reconstructed from a single gradient query.
A machine-learning approach solves CS data reconstruction for structural health monitoring.
problem Optimal solution for sparse optimization in compressive sensing.
method Formalizing CS data reconstruction as a supervised-learning task, using l1-norm regularization and a multi-neuron layer.
result High reconstruction accuracy achieved by the machine learning-based approach.
A neural network learns a convex regularizer for better image reconstruction.
problem Improving image reconstruction in inverse problems.
method Adversarial training of a data-adaptive ICNN as a convex regularizer.
result The convex regularizer leads to better convergence and error reduction in image reconstruction.
Study benchmarks methods for learning non-Cartesian k-space trajectories and reconstruction.
problem Benchmarking methods for learning non-Cartesian k-space trajectories and reconstruction.
method Comparing PILOT, BJORK, and HybLearn schemes to learn non-Cartesian k-space trajectories and reconstruction.
result HybLearn scheme outperforms other methods in learning and comparing non-Cartesian k-space trajectories and reconstruction.
SANs use sparse activation functions to minimize model complexity.
problem Model complexity in unsupervised learning.
method Introduce φ metric, define activation functions, present Sparsely Activated Networks (SANs).
result SANs with selected activation functions have small description length and interpretable kernels.
This work uses diffusion models for accurate signal recovery from semi-parametric models.
problem Recovering signals from semi-parametric single index models with discontinuous link functions.
method Proposes an efficient reconstruction method using diffusion models that requires one round of sampling and inversion.
result Demonstrates more accurate reconstructions with fewer evaluations compared to competing methods.
Proposes TCWAE to learn disentangled representations using the Wasserstein Autoencoder.
problem Balancing reconstruction fidelity and disentanglement in learning representations.
method TCWAE (Total Correlation Wasserstein Autoencoder) using different KL estimators.
result Competitive results on data sets with known generative factors, and improved reconstructions on unknown factors.
New DG method minimizes barycentric alignment and reconstruction loss.
problem Improving domain generalization in machine learning.
method Introduces a new upper bound and WBAE algorithm.
result WBAE outperforms state-of-the-art DG algorithms.
Algebras of smooth functions help reconstruct bulk topological types.
problem Reconstructing the smooth topological type of a compact manifold from its boundary.
method Introducing subalgebras of boundary functions and proving their tensor product reconstruction of the bulk algebra.
result The topological algebras A(v) and B(f) allow for the recovery of the smooth topological type of the bulk X. 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.