arXiv research

New spectral clustering method for multi-layer networks improves accuracy.

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 methods estimate mixed memberships in multi-layer networks.

This work tackles asymmetric community estimation in multi-layer directed networks.

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…

A new tensor network method for image classification reduces computation cost.

New view: Deep GCNs learn to anti-oversmooth during training.

MCSAE improves speaker embedding by focusing on both high- and low-level features.

The worldwide trade network has been widely studied through different data sets and network representations with a view to better understanding interactions among countries and products. Here we investigate international trade through the lenses of the single-layer, multiplex, and multi-layer networks. We discuss diffe…

New method preserves privacy while detecting communities in distributed networks.

The paper introduces a new loss function to prevent overfitting in semi-supervised graph networks.

New method detects OOD samples using neural network trajectories.

Two spectral clustering methods for multi-layer networks are analyzed and compared.

A hierarchical model shows how scaling laws emerge from sequential feature recovery.

TWIST algorithm detects communities in multi-layer networks with tensor decomposition.

Proposes a balanced multi-component and multi-layer neural network for efficient function approximation.

New AMP algorithms improve multi-layer signal reconstruction.

New model for multi-layer categorical data improves latent class analysis.

Learned data models based on sparsity are widely used in signal processing and imaging applications. A variety of methods for learning synthesis dictionaries, sparsifying transforms, etc., have been proposed in recent years, often imposing useful structures or properties on the models. In this work, we focus on sparsif…

Deep forests enhance expressiveness exponentially with depth, not width or tree size.

Paper explores statistical and computational limits of estimating low-rank Gaussian mixtures.

Graph embedding is an important approach for graph analysis tasks such as node classification and link prediction. The goal of graph embedding is to find a low dimensional representation of graph nodes that preserves the graph information. Recent methods like Graph Convolutional Network (GCN) try to consider node attri…

Paper presents ML-VAMP for efficient multi-layer inference with exact performance analysis.

Algorithm predicts performance of learning in multi-layer networks with matrix-valued hidden variables.

AMEIR automates recommender system design using NAS.

Single-layer GCN model improves recommendation performance with less complexity.

Paper optimizes clustering for multi-layer networks and discrete mixtures.

FMMNN combines sine activations with multi-component, multi-layer structure for high-frequency function approximation.

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…

Multi-layer optical film has been found to afford important applications in optical communication, optical absorbers, optical filters, etc. Different algorithms of multi-layer optical film design has been developed, as simplex method, colony algorithm, genetic algorithm. These algorithms rapidly promote the design and …

This work defines a new function space for multi-layer neural networks.

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…

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…

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 …

GCNs improve multi-layer network classification by expanding the distance between means.

Paper proposes a method to predict disk failures using multi-layer domain adaptive learning.

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…

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…

Automated classification of metadata of research data by their discipline(s) of research can be used in scientometric research, by repository service providers, and in the context of research data aggregation services. Openly available metadata of the DataCite index for research data were used to compile a large traini…

FPGA-based multi-layer equalizer adapts to changing channels.

New Banach spaces for ReLU networks enable better function approximation and gradient dynamics analysis.

Bipartite graphs have been used to represent data relationships in many data-mining applications such as in E-commerce recommendation systems. Since learning in graph space is more complicated than in Euclidian space, recent studies have extensively utilized neural nets to effectively and efficiently embed a graph's no…

SRR detects early signs of financial crises using multi-layer graphs.

How can local-search methods such as stochastic gradient descent (SGD) avoid bad local minima in training multi-layer neural networks? Why can they fit random labels even given non-convex and non-smooth architectures? Most existing theory only covers networks with one hidden layer, so can we go deeper? In this paper, w…

Lookahead pruning extends single-layer optimization to multi-layer, outperforming magnitude-based pruning.

Improved recommendation systems using multi-layer embeddings reduce model size while maintaining accuracy.

Spiking neural networks (SNNs) have garnered a great amount of interest for supervised and unsupervised learning applications. This paper deals with the problem of training multi-layer feedforward SNNs. The non-linear integrate-and-fire dynamics employed by spiking neurons make it difficult to train SNNs to generate de…

Deep generative priors are a powerful tool for reconstruction problems with complex data such as images and text. Inverse problems using such models require solving an inference problem of estimating the input and hidden units of the multi-layer network from its output. Maximum a priori (MAP) estimation is a widely-use…