New algorithm for signal reconstruction from multi-layered measurements.
problem Reconstructing signals from multi-layered non-linear measurements.
method Multi-Layer Approximate Message Passing (ML-AMP) algorithm and state evolution equations.
result Asymptotic free energy and minimal achievable error derived.
This paper optimizes multi-layer reinsurance policies to minimize risk measures.
problem Minimizing risk for insurance companies with multiple layers of reinsurance.
method Generalizes optimal stop-loss reinsurance to multi-layer policies using conditional tail expectation (CTE) risk measure.
result An optimal multi-layer reinsurance policy can be derived and estimated.
This work defines a new function space for multi-layer neural networks.
problem Characterizing the function space of multi-layer neural networks.
method Defining a neural Hilbert ladder (NHL) as an infinite union of reproducing kernel Hilbert spaces (RKHSs).
result Established theoretical properties of the new function space, including generalization guarantees and dynamics of random fields.
New multi-layer transform models improve image denoising.
problem Improving image denoising techniques.
method Developed multi-layer transform learning algorithms.
result Multi-layer models outperform single-layer schemes in image denoising.
Two spectral clustering methods for multi-layer networks are analyzed and compared.
problem Community detection in multi-layer networks.
method Sum and debiased sum of squared adjacency matrices for spectral clustering.
result Debiased sum of squared adjacency matrices outperforms sum of adjacency matrices.
Method infers multi-layer networks from gene expression data.
problem Inference of multi-level networks from gene expression data.
method Extension of latent graphical lasso method leveraging group structure.
result Efficacy in retrieving multi-layer network structure from synthetic data.
MGCN improves multi-layer graph classification using node attributes and relations.
problem Lack of comprehensive multi-layer graph embedding methods considering node attributes and different types of edges.
method Proposes MGCN, a method that combines GCN for multi-layer graphs, incorporating node attributes and both within and between layer relations.
result MGCN outperforms other multi-layer and single-layer methods in semi-supervised node classification tasks.
New methods estimate mixed memberships in multi-layer networks.
problem Complex community structure in multi-layer networks.
method Spectral methods using eigen-decomposition of aggregate matrices.
result Theoretical guarantees and empirical validation for mixed membership estimation.
Multi-layered networks link financial and macroeconomic dynamics.
problem Understanding the relationship between financial and macroeconomic dynamics.
method Multi-layered networks and network theory coupled with econometric techniques.
result Core-periphery structures in financial and macroeconomic networks are similar.
TWIST algorithm detects communities in multi-layer networks with tensor decomposition.
problem Community detection in multi-layer networks with multiple node-modality relationships.
method Tensor-based TWIST algorithm for global/local node and layer memberships.
result Accurate community detection with small misclassification error as network size increases.
This work tackles asymmetric community estimation in multi-layer directed networks.
problem Estimating different numbers of sender and receiver communities in multi-layer directed networks.
method Proposes a goodness-of-fit test based on the largest singular value of an aggregated normalized residual matrix.
result Develops sequential and ratio-based testing procedures to consistently determine true sender and receiver community numbers.
Proposes a balanced multi-component and multi-layer neural network for efficient function approximation.
problem Accurately and efficiently approximating complex functions with high degrees of freedom and computational cost.
method Inspired by a multi-component approach, MMNN combines single-layer networks with a multi-layer decomposition strategy.
result Significant reduction in training parameters, more efficient training process, and improved accuracy compared to FCNNs or MLPs.
New AMP algorithms improve multi-layer signal reconstruction.
problem Reconstructing signals and hidden variables from multi-layer networks with rotationally invariant weights.
method Developed multi-layer rotationally invariant generalized AMP (ML-RI-GAMP) algorithms and state evolution recursion.
result ML-RI-GAMP outperforms existing methods in terms of lower complexity and similar performance.
New model for multi-layer categorical data improves latent class analysis.
problem Traditional latent class analysis for single-layer categorical data is insufficient for multi-layer data.
method Developed a multi-layer latent class model (multi-layer LCM) and three spectral methods for estimation.
result The debiased sum of Gram matrices method performs best in estimating latent classes.
Proposes a method for multi-layered network embeddings that improves performance.
problem Mapping multi-layered network nodes into a vector space while preserving relationships.
method Jointly embed nodes in all layers via DeepWalk on a supra graph, then fine-tune embeddings for cohesive structure.
result Outperforms existing single- and multi-layered network embedding algorithms on benchmarks.
New capacity measure based on Fisher-Rao norm for neural networks.
problem Understanding the complexity and capacity of neural networks.
method Introducing Fisher-Rao norm and studying its invariance properties.
result The Fisher-Rao norm serves as an umbrella for existing norm-based complexity measures.
Proposes GrAMME for semi-supervised learning with multi-layered graphs.
problem Semi-supervised learning with multi-layered graphs.
method Uses attention models for feature learning in multi-layered graphs.
result Significant performance improvements over state-of-the-art network embedding strategies.
Paper presents ML-VAMP for efficient multi-layer inference with exact performance analysis.
problem Inference in multi-layer deep neural networks with non-convex optimization.
method ML-VAMP algorithm for MAP and MMSE estimates, with performance predictions in high dimensions.
result ML-VAMP achieves Bayes-optimal MSE under certain conditions, providing exact performance characterization.
New methods for community detection in multi-layer networks improve upon existing techniques.
problem Estimating a consensus community structure in multi-layer networks.
method Spectral clustering and matrix factorization methods for low-rank matrix optimization.
result Consistency properties of intermediate fusion techniques under multi-layer stochastic blockmodel.
Enhances relational reasoning with multi-layer architecture.
problem Limited relational reasoning with shallow architectures.
method Multi-layer relation network architecture.
result Solved all 20 tasks in bAbI 20 QA dataset.
New AI algorithm improves multi-layer optical film design efficiency.
problem Traditional algorithms converge to local optima, limiting global optimal solutions.
method Deep Q-learning for global optimal multi-layer optical film design.
result Deep Q-learning model converges global optimum of optical thin film structure.
In recent years there has been an increased interest in statistical analysis of data with multiple types of relations among a set of entities. Such multi-relational data can be represented as multi-layer graphs where the set of vertices represents the entities and multiple types of edges represent the different relatio…
Algorithm predicts performance of learning in multi-layer networks with matrix-valued hidden variables.
problem Signal recovery and learning in multi-layer neural networks with matrix-valued hidden variables.
method Unified approximation algorithm for MAP and MMSE inference, extending ML-VAMP to handle matrix-valued unknowns.
result Performance of ML-Mat-VAMP algorithm can be predicted in a random large-system limit.
New spectral clustering method for multi-layer networks improves accuracy.
problem Detecting community structure in multi-layer networks.
method Integrative spectral clustering based on adaptive layer aggregation.
result Our methods minimize mis-clustering error and outperform existing methods.
Paper trains multi-layer SNNs using NormAD for spatio-temporal error backpropagation.
problem Training multi-layer SNNs with non-linear integrate-and-fire dynamics.
method Formulates training as optimization, uses NormAD for iterative synaptic weight update.
result Validated on 2- and 3-layer SNNs solving spike-based XOR and generic problems.
New multi-layer algorithm improves CNN performance.
problem Efficiently modeling and processing information with parsimonious representations.
method Generalized Basis Pursuit to multi-layer setting, proposing ML-ISTA and ML-FISTA algorithms.
result Nested first order algorithms converge to solve the multi-layer problem.
Combines multi-layer graphs to infer global network structure.
problem Leveraging domain knowledge in structure inference for multi-layer graphs.
method Mask combination of multi-layer graphs using optimization.
result Enhanced structure inference through multi-layer graph integration.
Paper optimizes clustering for multi-layer networks and discrete mixtures.
problem Optimizing clustering in multi-layer networks and discrete mixtures.
method Two-stage method: tensor-based initialization and likelihood-based refinement.
result Achieves minimax optimal error rate for multi-layer networks and discrete mixtures.
FMMNN combines sine activations with multi-component, multi-layer structure for high-frequency function approximation.
problem Effective representation and learning of high-frequency features in neural networks.
method Introduces FMMNN with sine-type activations and multi-component, multi-layer structure.
result FMMNN achieves strong accuracy and favorable convergence on oscillatory function-approximation benchmarks.
Inpatient2Vec learns representations for inpatients with multi-layer self-attention.
problem Lack of specialized RL methods for inpatient data with strong temporal relations and consistent diagnoses.
method Inpatient2Vec uses a multi-layer self-attention mechanism with two training tasks to learn medical activity, hospital day, and diagnosis representations for inpatients.
result Inpatient2Vec outperforms baselines on semantic similarity and clinical events prediction tasks.
Method predicts motor symptoms of Parkinson's disease from daily activities.
problem Objective monitoring of Parkinson's disease symptoms.
method Multi-layer Gaussian process models for three types of movement abnormalities.
result Strong agreement between model predictions and clinical annotations.
A novel multi-layer architecture for one-class classification using graph-embedded kernel ridge regression.
problem Outlier detection in one-class classification using only normal samples.
method Stacking various Graph-Embedded Kernel Ridge Regression (KRR) based Auto-Encoders in a hierarchical fashion.
result The proposed method outperforms existing one-class classifiers on 21 benchmark datasets.
The study estimates the expressiveness of GCNs with bounds on the number of linear regions.
problem Characterizing the expressiveness of graph convolutional networks (GCNs).
method Estimates the number of linear regions for one-layer and multi-layer GCNs.
result GCNs with multiple layers have exponentially more expressivity per parameter than one-layer GCNs.
Paper analyzes convergence of SGD in RNNs, proving linear convergence rate.
problem Analyzing convergence of SGD in multi-layer recurrent neural networks.
method Developed tools to analyze multi-layer networks with ReLU activations.
result SGD achieves linear convergence rate in training RNNs with sufficient neurons.
GCNs improve multi-layer network classification by expanding the distance between means.
problem Improving multi-layer network classification with graphical information.
method Theoretical and empirical study of graph convolutions in multi-layer networks.
result Graph convolutions expand the classification regime by a factor of 1 / E m d e g 4 1/\sqrt[4]{\mathbb{E}{
m deg}} 1/ 4 E m d e g . A deep BSDE approach tackles multi-layered xVA calculations for portfolio valuation.
problem Computational intractability in nested simulations for multi-layered xVA calculations.
method Iterative deep BSDE approach, change-of-measure method, quantile regression for margin computation.
result Reduces computational demands and successfully scales to high-dimensional portfolios.
Paper proposes a method to predict disk failures using multi-layer domain adaptive learning.
problem Traditional machine learning models struggle to predict disk failures due to limited data.
method Multi-layer domain adaptive learning with source and target domains.
result The proposed method improves failure prediction accuracy on disk data with few failure samples.
Nestedness in global trade network revealed using multi-layer approach.
problem Understanding nested structures in international trade networks.
method Constructed a multi-layer network with countries, industries, and transactions; computed nestedness based on buyers' and sellers' involvement.
result Identified variations of nestedness over time and contributing countries and industries.
A new multi-layer attention mechanism improves speech keyword recognition accuracy.
problem Inaccurate attention weights in LSTM networks for speech keyword recognition.
method Introducing information from layers prior to feature extraction into attention weights calculations.
result The proposed multi-layer attention mechanism leads to more accurate attention weights and improved keyword spotting performance.
A generative model is developed for deep (multi-layered) convolutional dictionary learning. A novel probabilistic pooling operation is integrated into the deep model, yielding efficient bottom-up (pretraining) and top-down (refinement) probabilistic learning. Experimental results demonstrate powerful capabilities of th…
Model financial contagion in a multi-layered network with obligations in illiquid assets.
problem Model financial contagion in a multi-layered network with obligations in illiquid assets.
method Developed a multi-layered financial network model with fire sales, utility maximization, and tâtonnement process.
result Existence and uniqueness of equilibrium portfolio holdings and market prices in a multi-layered financial system.
FPGA-based multi-layer equalizer adapts to changing channels.
problem Real-time adaptation to time-varying channel impairments.
method Multi-layer machine learning on FPGA with on-chip gradient backpropagation training.
result Real-time adaptation to changing channel conditions achieved.
Predicts cellular functions in human tissues using multi-layer networks.
problem Challenges in predicting tissue-specific cellular function.
method Hierarchy-aware unsupervised node feature learning for multi-layer networks.
result Improves prediction accuracy of cellular functions in 48 tissues.
New Banach spaces for ReLU networks enable better function approximation and gradient dynamics analysis.
problem Function approximation and gradient dynamics in multi-layer ReLU networks.
method Developed Banach spaces for ReLU networks, defined new function representations, and analyzed gradient flow dynamics.
result Gradient flow dynamics of the new representation is the continuous analog of gradient descent for ReLU networks.
SRR detects early signs of financial crises using multi-layer graphs.
problem Predicting systemic financial transitions from evolving market interactions.
method Systemic Risk Radar (SRR) models financial markets as multi-layer graphs.
result Graph-derived features provide useful early-warning signals compared to feature-based models.
Lookahead pruning extends single-layer optimization to multi-layer, outperforming magnitude-based pruning.
problem Pruning neural networks to reduce computational cost and memory usage.
method Developed a multi-layer optimization approach extending the single-layer optimization of magnitude-based pruning.
result Consistently outperforms magnitude-based pruning on various networks, especially in high sparsity.
Improved recommendation systems using multi-layer embeddings reduce model size while maintaining accuracy.
problem Improving model accuracy in recommendation systems while minimizing model size.
method Introducing a multi-layer embedding training (MLET) architecture that trains embeddings via a sequence of linear layers.
result Substantial advantages in model accuracy and memory footprint are achieved with reduced embedding dimensions.
Based on the approach of flow distances, the international trade flow system is studied from the perspective of multi-layer flow network. A model of multi-layer flow network is proposed for modelling and analyzing multiple types of flows in flow systems. Then, flow distances are introduced, and symmetric minimum flow d…