Wavelet-based fANOVA method improves factor analysis.
problem Efficiently analyzing functional data with multiple factors.
method Bayesian hierarchical model with spike-and-slab mixture and NIG conjugate setup, combined with Markov grove graphical model.
result Method outperforms existing wavelet-domain fANOVA methods in various settings.
A new algorithm for optimizing huge-scale black-box problems with reduced memory usage.
problem Optimizing huge-scale black-box problems with limited vector operations.
method ZO-BCD algorithm for zeroth-order optimization with reduced memory footprint.
result ZO-BCD achieves state-of-the-art adversarial attack success rate of 97.9%.
Paper defends iris recognition from adversarial examples using wavelet decomposition.
problem Adversarial examples threaten deep neural networks in biometric applications.
method Wavelet domain denoising of input examples to detect and mitigate adversarial attacks.
result Proposed defense strategies improve recognition accuracy against adversarial attacks.
Unified framework for feature-based explanations using ANOVA and game theory.
problem Differences between feature-based explanations methods limit their applicability.
method Introduces a unified framework combining fANOVA and cooperative game theory.
result Uncovered similarities and differences between various explanation techniques.
GAMI-Tree uses model-based trees to fit low-order fANOVA models.
problem Fitting interpretable fANOVA models with low-order interactions.
method GAMI-Tree uses model-based trees and a new interaction filtering method.
result GAMI-Tree outperforms EBM and GAMI-Net in predictive performance and interpretability.
Proposes FOAGP for efficient orthogonal effect decomposition of black-box computer experiments.
problem Challenges in sensitivity analysis of black-box computer experiments with complex, nonlinear functional outputs.
method Functional-output orthogonal additive Gaussian process (FOAGP) with conditional orthogonality constraint.
result Demonstrates effectiveness in orthogonal effect decomposition and variance decomposition through simulations and real-world application.
WCAM assesses neural network reliability by attributing decisions to wavelet scales.
problem Challenges in evaluating neural network reliability and feature robustness.
method Introduces WCAM, a wavelet-based attribution method to assess decision reliability.
result WCAM reveals where and on what scales a model focuses, enabling reliable decision assessment.
New ML algorithms improve model interpretability without sacrificing performance.
problem Lack of interpretability in complex machine learning models.
method Developed new algorithms based on fANOVA framework, including GAMI-Lin-T and GAMI-Net.
result GAMI-Lin-T and GAMI-Net perform comparably to EBM and better in interpretability.
A cost-effective method to generate high-resolution images using wavelet-based super-resolution.
problem High compute resources required for training state-of-the-art image generation models.
method Wavelet-based two-step training framework: low-frequency sampling followed by super-resolution.
result Achieves FID of 10.59 on ImageNet 512x512 with half the compute of BigGAN.
Lap-VARD algorithm improves signal reconstruction in tomography.
problem Signal reconstruction in tomography with sparsity constraint.
method Laplacian prior and automatic relevance determination framework.
result Optimized Laplacian prior enhances balance between sparsity and accuracy.
Unified method for simultaneous denoising and clustering.
problem Clustering noisy signals.
method Sparse convex wavelet clustering with fusion and group-sparse penalties.
result Unified approach that denoises and clusters simultaneously.
Paper explores deep, convolutional, and recurrent models for HAR using wearable sensors.
problem Human activity recognition across various tasks using wearable sensors.
method Rigorous exploration of deep, convolutional, and recurrent models on three datasets.
result Recurrent models outperform state-of-the-art on a large benchmark dataset.
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.
Telemonitoring of electroencephalogram (EEG) through wireless body-area networks is an evolving direction in personalized medicine. Among various constraints in designing such a system, three important constraints are energy consumption, data compression, and device cost. Conventional data compression methodologies, al…
HaarPooling compresses graphs by Haar transforms, improving graph classification and regression.
problem Handling graphs of varying size and structure in GNNs.
method HaarPooling, a cascade of clusterings and compressive Haar transforms.
result HaarPooling synthesizes graph features into uniform size, achieving state-of-the-art performance.
Wavelet Attribution Method (WAM) improves feature attribution for deep models.
problem Inability of pixel-based heatmaps to capture data structure and variability in feature attribution.
method Wavelet domain for feature attribution, leveraging spatial and scale-localized properties of wavelet coefficients.
result WAM provides quantitatively superior explanations across audio, image, and volume modalities.
AWD distills neural network info into interpretable wavelets.
problem Imbalanced interpretability and efficiency in deep learning models.
method Adaptive wavelet distillation (AWD) penalizes neural network attributions in wavelet domain.
result AWD yields a concise, efficient, and interpretable model.
Proposes a hierarchical deep generative model for natural images.
problem Analyzing piecewise smooth signals like natural images.
method Hierarchical deep generative model with alternating minimization algorithm.
result Demonstrates the model's representation capabilities and classification performance.
MLShrink integrates machine learning with wavelet shrinkage for denoising.
problem Denoising signals with uncertain magnitudes
method Combines wavelet shrinkage with machine learning
result Preserves simplicity for signal coefficients while allowing data-adaptive decisions for ambiguous coefficients
Proposes SVR-based image denoising using natural image relations.
problem Image denoising with arbitrary noise sources.
method Support Vector Regression (SVR) in wavelet domain, enforcing natural image relations.
result Outperforms conventional methods and is similar to state-of-the-art methods for Gaussian noise.