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.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,051 papers · 148 categories
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48 results for “multi-layer models”
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.
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.
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.
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.
Community detection in multi-relational data with restricted multi-layer stochastic blockmodelstat.ML
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…
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 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.
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.
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.
Optimal Clustering of Discrete Mixtures: Binomial, Poisson, Block Models, and Multi-layer Networksmath.ST
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.
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.
We consider the problem of reconstructing a signal from multi-layered (possibly) non-linear measurements. Using non-rigorous but standard methods from statistical physics we present the Multi-Layer Approximate Message Passing (ML-AMP) algorithm for computing marginal probabilities of the corresponding estimation proble…
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.
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…
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 .
Modern data analysis pipelines are becoming increasingly complex due to the presence of multi-view information sources. While graphs are effective in modeling complex relationships, in many scenarios a single graph is rarely sufficient to succinctly represent all interactions, and hence multi-layered graphs have become…
Multi-layered representation is believed to be the key ingredient of deep neural networks especially in cognitive tasks like computer vision. While non-differentiable models such as gradient boosting decision trees (GBDTs) are the dominant methods for modeling discrete or tabular data, they are hard to incorporate with…
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
Parsimonious representations are ubiquitous in modeling and processing information. Motivated by the recent Multi-Layer Convolutional Sparse Coding (ML-CSC) model, we herein generalize the traditional Basis Pursuit problem to a multi-layer setting, introducing similar sparse enforcing penalties at different representat…
A usual reinsurance policy for insurance companies admits one or two layers of the payment deductions. Under optimal criterion of minimizing the conditional tail expectation (CTE) risk measure of the insurer's total risk, this article generalized an optimal stop-loss reinsurance policy to an optimal multi-layer reinsur…
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…
A new method learns hierarchical EBM models with diffusion schemes.
problem Challenges in learning EBM models with multi-modal distributions.
method Proposes a diffusion probabilistic scheme to learn EBM models in hierarchical latent spaces.
result Demonstrates superior performance on various tasks with diffusion-learned EBM.
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.
Deep Transformed Gaussian Processes extend TGPs with variational inference for scalable multi-layer modeling.
problem Flexible modeling of complex data distributions.
method DTGPs are a multi-layer model of TGPs using variational inference for scalability.
result DTGPs achieve good scalability and performance in multiple regression datasets.
Motivation: Understanding functions of proteins in specific human tissues is essential for insights into disease diagnostics and therapeutics, yet prediction of tissue-specific cellular function remains a critical challenge for biomedicine. Results: Here we present OhmNet, a hierarchy-aware unsupervised node feature le…
A generative Bayesian model is developed for deep (multi-layer) convolutional dictionary learning. A novel probabilistic pooling operation is integrated into the deep model, yielding efficient bottom-up and top-down probabilistic learning. After learning the deep convolutional dictionary, testing is implemented via dec…
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.
Inferencing with network data necessitates the mapping of its nodes into a vector space, where the relationships are preserved. However, with multi-layered networks, where multiple types of relationships exist for the same set of nodes, it is crucial to exploit the information shared between layers, in addition to the …
Spectral and matrix factorization methods for consistent community detection in multi-layer networksstat.ML
We consider the problem of estimating a consensus community structure by combining information from multiple layers of a multi-layer network using methods based on the spectral clustering or a low-rank matrix factorization. As a general theme, these "intermediate fusion" methods involve obtaining a low column rank matr…
New method provides exact performance guarantees for MAP inference in deep networks.
problem Analyzing MAP inference in deep networks with complex data.
method Multi-layer vector approximate message passing (ML-VAMP) method.
result Mean squared error of ML-VAMP estimate can be exactly characterized in high-dimensional random limit.
New method initializes sigmoidal MLPs for interpretable shapes.
problem Creating interpretable decision boundaries in neural networks.
method Introducing a geometry-aware initialization for sigmoidal multi-layer perceptrons (MLPs) using tropical geometry.
result Sigmoidal MLPs can have decision boundaries aligned with prescribed shapes at initialization.
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.
New method amplifies hidden structure in complex networks.
problem Difficulty in uncovering hidden structure in complex networks.
method Iterative weakening of dominant structure through randomization.
result Theoretical support for the effectiveness of structure amplification.
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.
Transformers can learn spectral methods and perform unsupervised learning.
problem Learning spectral methods using unsupervised learning.
method Using multi-layered Transformers, pre-trained on a large set of instances, to learn and perform statistical estimation tasks.
result Proven that pre-trained Transformers can learn spectral methods and perform tasks like PCA and clustering.
DeepAM optimizes deep neural networks for image super-resolution.
problem Image super-resolution using deep neural networks.
method L-layer analysis dictionary model with IPAD and CAD.
result DeepAM outperforms deep neural networks with back-propagation.