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Found 12 papers, 4 papers with code
Network Alignment with Transferable Graph Autoencoders
Network alignment is the task of establishing one-to-one correspondences between the nodes of different graphs and finds a plethora of applications in high-impact domains.
Transferability of Convolutional Neural Networks in Stationary Learning Tasks
To accomplish this we investigate the properties of CNNs for tasks where the underlying signals are stationary.
Solving Large-scale Spatial Problems with Convolutional Neural Networks
Over the past decade, deep learning research has been accelerated by increasingly powerful hardware, which facilitated rapid growth in the model complexity and the amount of data ingested.
Multi-Target Tracking with Transferable Convolutional Neural Networks
Multi-target tracking (MTT) is a classical signal processing task, where the goal is to estimate the states of an unknown number of moving targets from noisy sensor measurements.
Representation Power of Graph Neural Networks: Improved Expressivity via Algebraic Analysis
Despite the remarkable success of Graph Neural Networks (GNNs), the common belief is that their representation power is limited and that they are at most as expressive as the Weisfeiler-Lehman (WL) algorithm.
Space-Time Graph Neural Networks
We introduce a generic definition of convolution operators that mimic the diffusion process of signals over its underlying support.
GAGE: Geometry Preserving Attributed Graph Embeddings
Various real-world networks include information about both node connectivity and certain node attributes, in the form of features or time-series data.
TeX-Graph: Coupled tensor-matrix knowledge-graph embedding for COVID-19 drug repurposing
Knowledge graphs (KGs) are powerful tools that codify relational behaviour between entities in knowledge bases.
Generalized Canonical Correlation Analysis: A Subspace Intersection Approach
It is shown that from a linear algebra point of view, GCCA is tantamount to subspace intersection; and conditions under which the common subspace of the different views is identifiable are provided.
PREMA: Principled Tensor Data Recovery from Multiple Aggregated Views
The goal of this paper is to reconstruct finer-scale data from multiple coarse views, aggregated over different (subsets of) dimensions.
Structured SUMCOR Multiview Canonical Correlation Analysis for Large-Scale Data
In this work, we propose a new computational framework for large-scale SUMCOR GCCA that can easily incorporate a suite of structural regularizers which are frequently used in data analytics.
Hyperspectral Super-Resolution: A Coupled Tensor Factorization Approach
Third, the majority of the existing methods assume that there are known (or easily estimated) degradation operators applied to the SRI to form the corresponding HSI and MSI--which is hardly the case in practice.
