Generative adversarial network improves signal reconstruction from magnitude spectrograms.
problem Reconstructing a time-domain signal from a magnitude spectrogram.
method Deep neural network and generative adversarial network approach.
result Our method reconstructs signals faster with higher quality than the Griffin-Lim method.
Paper presents a method for robust surface reconstruction from noisy gradients using adaptive dictionary learning.
problem Reconstructing surfaces from noisy photometric stereo normal vector maps.
method Adaptive dictionary learning to sparsely represent spatial patches of the surface, enforcing smoothness constraints.
result The method effectively learns the underlying surface structure and is robust to noise.
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.
Unified deep learning approach for time series forecasting using VMD-CNN-LSTM.
problem Time series forecasting problem.
method Proposes a unified deep learning approach with decomposition-reconstruction-ensemble framework using VMD-CNN-LSTM.
result The proposed approach outperforms benchmark approaches in forecasting accuracy.
Deep learning speeds up MRI image reconstruction from sparse data.
problem Efficiently reconstructing MRI images from limited data.
method Data-driven, model-driven, and integrated deep learning approaches.
result Potential for deep learning to significantly speed up MRI reconstruction.
Study proposes CNN for reconstructing high-res urban DEMs.
problem Lack of high-res urban DEM datasets for flood modeling.
method Multi-scale CNN model trained on urban DEMs of varying resolutions.
result CNN-based method produces superior high-res urban DEMs.
EggNet reconstructs particle tracks from hits using evolving graph attention networks.
problem Particle track reconstruction is computationally expensive and combinatorial.
method EggNet uses a one-shot object condensation approach with evolving graph attention networks.
result EggNet outperforms methods requiring fixed input graphs on TrackML dataset.
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.
New method reconstructs networks from spatiotemporal data.
problem Network reconstruction from spatiotemporal data.
method Multivariate Hawkes processes using both temporal and spatial information.
result Spatiotemporal approach yields improved network reconstruction.
New methods for signal reconstruction using guiding sets and frame-less pathways.
problem Signal reconstruction in Hilbert spaces with specified properties.
method Axiomatic approach involving sample consistent and guiding sets, with reconstruction set defined as a shortest pathway.
result Existence and uniqueness of reconstruction set in Hilbert space, with derived stability and error bounds.
We focus on an interpolation method referred to Bayesian reconstruction in this paper. Whereas in standard interpolation methods missing data are interpolated deterministically, in Bayesian reconstruction, missing data are interpolated probabilistically using a Bayesian treatment. In this paper, we address the framewor…
Linear reconstruction works for MRI compression without prior signal knowledge.
problem Compressive MRI reconstruction without signal structure knowledge.
method Learn sub-sampling pattern from training data, use linear reconstruction.
result Theoretical and experimental validation of linear reconstruction effectiveness.
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.
New method reconstructs signals and images from periodic nonlinearities.
problem Reconstructing signals and images from periodic nonlinearities.
method Design of measurement scheme for efficient reconstruction, adaptable to compressive sensing.
result Effective reduction in measurement complexity for HDR imaging with minimal quality loss.
RECON reconstructs regulatory networks from time-course data, reducing spurious edges and preserving true regulatory edges.
problem Reconstructing regulatory networks from time-course data with minimal spurious edges and preserving true regulatory relationships.
method RECON uses an integral-based additive nonparametric ODE model with five methodological advances to reconstruct regulatory networks.
result RECON consistently outperforms existing methods, reducing spurious edges and preserving true regulatory edges across various scenarios.
Deep learning improves 3D reconstruction from sparse X-ray views.
problem Sparse view CT reconstruction produces severe streaking artifacts.
method Proposes a deep learning architecture for 3D reconstruction from 9 views.
result Superior reconstruction performance confirmed with real data.
Neurally Augmented ALISTA improves sparse reconstruction performance.
problem Improving sparse reconstruction performance with theoretical guarantees and empirical improvements.
method Integrates an LSTM network to compute adaptive step sizes and thresholds for each target vector during reconstruction.
result Empirical performance is further improved, especially as compression ratios become more challenging.
New algorithm reconstructs genealogies from genetic data.
problem Reconstructing genealogies from genetic data.
method Iterative algorithm {\sc Rec-Gen} for pedigrees from a generative model.
result Accurate reconstruction of a large fraction of pedigrees with low sample complexity.
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.
New method reconstructs networks with unknown and varying errors.
problem Network datasets often contain errors and omissions, limiting traditional analysis.
method Bayesian reconstruction approach that handles heterogeneous errors and single edge measurements.
result Efficient nonparametric inference for hierarchical community structure from noisy data.
New method reconstructs data subsets from limited published statistics.
problem Reconstructing tabular data from aggregate statistics when full datasets are not possible.
method Generates and verifies subsets of rows and columns that are guaranteed to be correct.
result Privacy violations can persist even with sparse published statistics.
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.
Proposes a new approach to learn predicates from data.
problem Predicate invention in relational and deep learning communities.
method Theory reconstruction approach extending autoencoder to relational settings.
result Starts a discussion for a unified framework for predicate invention.
ECLAIR improves lineage reconstruction from single-cell data with uncertainty estimates.
problem Uncertainty in cell lineage reconstruction from high-dimensional single-cell data.
method ECLAIR uses an ensemble approach to improve robustness and provide uncertainty estimates.
result ECLAIR successfully reconstructs known lineage relationships and improves robustness of predictions.
Deep learning improves ROI reconstruction in low-dose CT.
problem Severe cupping artifacts in standard analytic reconstruction.
method Proposes a deep learning architecture to remove null space signals from FBP reconstruction.
result Near-perfect reconstruction with 7-10 dB improvement in PSNR.
RADAR uses diffusion models to detect anomalies without reconstruction, improving accuracy and efficiency.
problem Challenges in anomaly detection and segmentation, especially in real-time applications.
method RADAR uses attention-based diffusion models to directly produce anomaly maps from the diffusion process, bypassing reconstruction.
result RADAR improves F1 score by 7% on MVTec-AD and 13% on 3D-printed material compared to state-of-the-art methods.
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.
New method reconstructs signals from modulo observations.
problem Reconstructing signals from under-determined modulo observations.
method Proposes a novel algorithm inspired by phase retrieval for under-determined signal reconstruction.
result Successfully recovers signals with improved performance over existing methods.
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.
Enhances deep neural networks for MRI reconstruction by increasing expressivity.
problem Balancing network complexity and performance in deep learning MRI reconstruction.
method Geometric approach using bootstrapping and subnetwork aggregation with attention module.
result Significant improvement in MRI reconstruction performance with minimal complexity increase.
New networks improve MRI image reconstruction without calibration.
problem Calibration issues in parallel MR image reconstruction.
method Data consistency layers in deep CNN networks.
result Proposed methods outperform existing techniques.
Generative adversarial networks reconstruct MRI images without full data.
problem Lack of fully-sampled ground truth data for supervised MRI reconstruction.
method Generative adversarial networks for unsupervised MRI reconstruction.
result Reconstructed images show more anatomical structure than conventional methods.
Graph filtering improves data reconstruction performance.
problem Data reconstruction and dimensionality reduction.
method Formulate data tasks as graph filtering operations, optimize mean-square error cost involving adjacency matrix, update filters via gradient descent.
result Better reconstruction performance of novel method compared to PCA.
Convolutional neural network improves MRE image reconstruction.
problem Reconstructing MRE images from displacement data is computationally intensive and costly.
method Proposes a CNN architecture to directly map MRE displacement data into elastograms, introducing a secondary loss for training.
result CNN-generated images compare favorably with nonlinear inversion methods.
New method reconstructs 3D protein structures from cryo-EM images.
problem Reconstructing continuous protein structures from noisy cryo-EM projections.
method Neural network-based approach that models structural heterogeneity in Fourier space.
result Demonstrated successful ab initio reconstruction of 3D protein complexes.
We consider the traffic data reconstruction problem. Suppose we have the traffic data of an entire city that are incomplete because some road data are unobserved. The problem is to reconstruct the unobserved parts of the data. In this paper, we propose a new method to reconstruct incomplete traffic data collected from …
New method regularizes MEG inverse problem for more accurate brain activity reconstruction.
problem Underdetermined inverse problem in MEG for precise brain activity reconstruction.
method Regularization using space-time separable Gaussian process model.
result Efficient and general Bayesian source reconstruction approach demonstrated.
Improved computed tomography reconstruction with deep learning and deep image prior.
problem Low data efficiency in computed tomography reconstruction.
method Combining learned primal-dual methods with deep image prior for improved quality and generalization.
result Proposed methods outperform state-of-the-art in low data regime.
New method reconstructs brain stimuli from responses.
problem Reconstructing perceived stimuli from brain responses.
method Combining probabilistic inference and adversarial training of neural networks.
result Generates state-of-the-art reconstructions of perceived faces.
In this article we extend the computational geometric curve reconstruction approach to curves in Riemannian manifolds. We prove that the minimal spanning tree, given a sufficiently dense sample, correctly reconstructs the smooth arcs and further closed and simple curves in Riemannian manifolds. The proof is based on th…
Bayesian framework for sparse signal reconstruction in astronomy and machine learning.
problem Signal reconstruction in noisy 1- and 2-dimensional signals, including astronomical images.
method Bayesian interpretation of sparse reconstruction, using priors and integer parameters for basis functions.
result Order-of-magnitude computational efficiency gains compared to traditional methods.
Convolutional neural networks make astronomical image reconstruction faster and more efficient.
problem Efficiently reconstructing astronomical images from noisy or incomplete data.
method Use of convolutional neural networks for image reconstruction.
result Neural networks enable a linear complexity prediction step, making reconstruction computationally efficient.
Machine learning reconstructs aerodynamic forces from noisy data.
problem Accurately modeling aerodynamic forces with limited or noisy data.
method Physics-informed Gaussian processes trained on noisy structural responses.
result Strong agreement between true and predicted aerodynamic loads.
RECol generates error columns to improve outlier detection.
problem Outlier detection in data with complex relationships.
method Generates reconstruction error columns for leave-one-out feature sets.
result Improves ROC-AUC and PR-AUC values of common outlier detection methods.
Proposes a new method for efficient model reconstruction with uncertain parameters.
problem Reconstructing models with latent variables or parameters of unknown distribution.
method Local squared Wasserstein-2 (W_2) method.
result Efficiently reconstructs output distributions from observation data.
Bayesian approach improves deep image prior for image reconstruction.
problem Improving performance of deep image prior for image reconstruction tasks.
method Derive Bayesian approach using stochastic gradient Langevin, showing asymptotic equivalence to Gaussian process prior.
result Improves denoising and impainting results for image reconstruction tasks.
Deep learning improves neutrino-nucleus interaction vertex reconstruction.
problem Vertex reconstruction of neutrino-nucleus interaction events.
method Combining energy and timing data for classification and regression tasks using deep learning.
result The model achieves 4.00% higher classification accuracy and 0.9919 higher regression accuracy than previous methods.
A new method reduces CT scan radiation exposure while improving image quality.
problem Reducing patient radiation exposure in CT scans while maintaining image quality.
method PWLS-ULTRA method that combines clustering and learning-based techniques.
result The method significantly improves image quality compared to existing methods.