DEER network improves few-view breast CT image reconstruction efficiency and quality.
problem Efficient and high-quality few-view breast CT image reconstruction.
method Deep Efficient End-to-end Reconstruction (DEER) network with low model complexity.
result DEER network achieves competitive image quality with significantly fewer parameters compared to state-of-the-art 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.
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.
New method improves network reconstruction accuracy and speed.
problem Inefficient lasso for weighted networks in noisy data.
method Variational Bayesian weighted linear regression.
result New method outperforms lasso in accuracy and speed.
Develops efficient algorithms for spatial field reconstruction and sensor selection in heterogeneous weather sensor networks.
problem Efficient spatial field reconstruction and query-based sensor set selection in heterogeneous sensor networks.
method Spatial Best Linear Unbiased Estimator (S-BLUE) and Cross Entropy method.
result Efficient algorithms with performance guarantees for spatial field reconstruction and sensor selection.
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.
PALMS reconstructs large-scale networks efficiently with parallel computing.
problem Reconstructing large-scale latent networks from observed dynamics is computationally challenging.
method PALMS (Parallel Adaptive Lasso with Multi-directional Signals) framework for distributed network reconstruction.
result PALMS substantially reduces computational complexity and storage requirements.
We propose an efficient algorithm for sparse signal reconstruction problems. The proposed algorithm is an augmented Lagrangian method based on the dual sparse reconstruction problem. It is efficient when the number of unknown variables is much larger than the number of observations because of the dual formulation. More…
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.
OnAIR reconstructs dynamic images from sparse measurements online.
problem Reconstructing dynamic images from limited or corrupted measurements.
method Online adaptive reconstruction using sparsity and low-rank models with dictionary learning.
result Memory-efficient online algorithms for sequential estimation of dictionary and images.
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.
Noise2Filter improves 3D tomography reconstruction efficiency and accuracy.
problem Efficiently reconstructing 3D tomographic images in real-time with limited data.
method Self-supervised learning and a learned filter method.
result Noise2Filter achieves real-time reconstruction with limited loss of accuracy.
MLPF uses graph neural networks to improve particle-flow reconstruction in high-pileup conditions.
problem Improving particle-flow reconstruction in high-pileup conditions at high-luminosity LHC.
method End-to-end trainable machine-learned particle-flow algorithm based on graph neural networks.
result MLPF improves physics response and demonstrates scalable reconstruction in high-pileup environments.
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.
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.
Paper presents efficient algorithms for reconstructing noisy pooled data.
problem Reconstructing hidden states from noisy pooled data.
method Simple and efficient distributed algorithms for two noise models.
result Our algorithms reconstruct exact initial states with high probability.
New algorithm learns stable LDSs with lower error and better control performance.
problem Learning stable LDSs from data with minimal reconstruction error and stability constraints.
method Proposes an optimization method using a recent characterization of stable matrices, iteratively improving reconstruction error and ensuring stability.
result Achieves orders-of-magnitude improvement in reconstruction error compared to existing methods.
This paper proposes a method to train multiple neural networks with shared parameters using a reconstruction loss.
problem Training multiple neural networks for correlated tasks separately is inefficient.
method Introduces a novel approach with a reconstruction loss to encourage shared features across multiple tasks.
result The proposed method achieves efficient transfer learning with competitive performance.
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.
Efficient deep learning for CT images reduces memory and training time.
problem Training deep neural networks for CT images is computationally expensive.
method Unrolled proximal gradient descent, replaced penalty terms with CNNs, used greedy learning with deep UNet and surrogate.
result Achieved comparable image quality to state-of-the-art methods on CT image reconstruction challenges.
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.
Paper speeds up visualization of uncertain data.
problem High computational cost in reconstructing data uncertainties.
method Subdivide data spatially, adaptively reconstructing only necessary values, using GPR kernel and saved data observations to estimate upper bounds for level-crossing probabilities.
result Accurate estimation of value occurrence probabilities with low computation cost.
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.
Improved particle-flow event reconstruction for future colliders using scalable neural networks.
problem Efficient and accurate particle reconstruction in future particle detectors.
method Comparative study of scalable machine learning models (graph neural network and kernel-based transformer) for event reconstruction.
result Graph neural network model improves jet transverse momentum resolution by up to 50%.
Gradient-based explanations can reveal model structure.
problem Tension between secrecy and model explanations.
method Algorithm to learn a two-layer ReLU network using gradient queries.
result The number of gradient queries is nearly optimal and independent of model size.
Neural net reconstructs dark matter density from halo velocities.
problem Reconstructing local dark matter density from halo velocities.
method Hybrid architecture combining U-Net and DeepSets.
result Hybrid network recovers density amplitudes and phases better than U-Net.
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.
Concrete autoencoder selects key features for efficient data reconstruction.
problem Efficiently identifying and selecting important features for data reconstruction.
method Concrete selector layer with temperature-controlled selection during training, followed by reconstruction using a standard neural network.
result Concrete autoencoder selects a small subset of genes that can reconstruct the remaining gene expression levels, improving on existing methods.
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.
This work tackles uncertainty quantification in tomography reconstruction.
problem Ill-posed nature of tomographic reconstruction leading to no unique solution.
method Gaussian process modeling to incorporate prior knowledge and experimental noises.
result Efficient uncertainty quantification in tomographic reconstruction.
PC-RNN reconstructs MRI images from undersampled data with more details.
problem Recovering fine details from undersampled MRI data.
method Pyramid Convolutional RNN (PC-RNN) with three ConvRNN modules for multi-scale reconstruction.
result PC-RNN outperforms other methods in recovering more details from MRI images.
Generative models improve image reconstruction and uncertainty quantification.
problem Bayesian inverse problems, especially image reconstruction from noisy and incomplete data.
method Data-driven priors and computationally tractable posterior analysis.
result Efficient uncertainty quantification without retraining for different corruption types.
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 …
SDSR reconstructs species trees from genetic markers efficiently.
problem Challenges in reconstructing species trees from genetic data.
method Spectral divide-and-conquer approach based on graph theory.
result SDSR achieves up to 10-fold faster runtime with comparable accuracy.
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.
Graph imputation neural network (GINN) augments datasets by reconstructing damaged nodes.
problem Efficient data augmentation in semi-supervised learning with limited labeled data.
method Graph-based neural network (GINN) for missing data imputation and data augmentation.
result GINN can significantly improve semi-supervised learning performance and augment datasets up to 10x.
Study proposes a new method for MRI image reconstruction using denoising autoencoders and undecimated wavelet transforms.
problem Efficient MRI image reconstruction using under-sampled data.
method Undecimated wavelet transform, denoising autoencoder, proximal gradient algorithm.
result The proposed method enhances MRI image reconstruction efficiency and robustness.
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.
A1GM method improves efficiency in reconstructing missing data using KL divergence.
problem Efficiently reconstructing missing data in matrices.
method Fast non-gradient-based rank-1 NMF using KL divergence.
result A1GM outperforms gradient methods in efficiency with competitive reconstruction errors.
Bayesian framework learns prior from data to quantify uncertainty in MRI reconstruction.
problem Quantifying uncertainty in deep learning solutions for inverse problems.
method Adopting denoising score matching to learn prior from data, using it in an annealed Hamiltonian Monte-Carlo scheme.
result The approach yields high-quality reconstructions and assesses uncertainty on specific features.
Enhances diffusion models by preprocessing data to improve reconstruction quality.
problem Slow sampling and poor reconstruction quality in diffusion models, especially for small-scale networks.
method Applying Gaussianization preprocessing to the training data to make the target distribution more Gaussian-like.
result Improves generation quality, especially in the early stages of reconstruction with small networks.
Topology-enhanced loss improves 3D object reconstruction from 2D images.
problem Challenges in reconstructing 3D objects from 2D images, especially capturing shape information.
method Integrates multi-scale topological features into the reconstruction loss using cubical complexes and optimal transport distance.
result Topology-aware loss substantially improves 3D reconstruction quality.
A new method learns high-frequency components for better image reconstruction.
problem Efficiently reconstructing feature details in under-sampled imaging.
method Proposes HF-DAEP, a denoising autoencoder using multi-profile high-frequency components.
result Demonstrates improved reconstruction of feature details in MRI and CT.
A novel method for feature selection using a reparameterized logitNormal distribution.
problem Feature selection for reconstruction in high-dimensional data.
method Introducing a reparameterization of the logitNormal distribution to address differentiability and covariance issues.
result The method provides an effective exploration scheme and efficient feature selection for reconstruction.
Optimizes parameter reconstruction for optical scatterometry using Gaussian process regression.
problem Efficiently reconstructing geometry parameters of micro/nanostructures from scatterometry measurements.
method Bayesian optimization with Gaussian-process regression to find optimal parameter values.
result Gaussian process regression accelerates the optimization process for numerical simulations.
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.
FsNet selects features for high-dimensional biological data efficiently.
problem Efficient feature selection for high-dimensional biological data.
method FsNet combines selection and reconstruction layers with tiny networks for weight prediction.
result FsNet outperforms standard DNNs on high-dimensional biological datasets.
Convolutional autoencoding improves long text reconstruction.
problem Text reconstruction quality decreases with text length.
method Sequence-to-sequence, purely convolutional and deconvolutional autoencoding.
result Better at reconstructing and correcting long paragraphs.