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In this talk, we will consider the distributed sparse identification problem over wireless sensor networks such that all sensors cooperatively estimate the unknown sparse parameter vector of stochasti...
Decentralized optimization algorithms save remarkable communication overheads in distributed deep learning since each node averages locally with neighbors. The network topology connecting all nodes de...
Tensor-based modeling and computation emerge prominently with urgent demands from practical applications in the big data era. With the intrinsic sparsity in real data sets and the dimensionality reduc...
Graphs play an important role in many fields of machine learning such as clustering. Many graph-based machine learning approaches assume that the graphs have hidden group structures. However, the grou...
Conventional inferential methods for (deep) Gaussian Processes models can suffer from high computational complexity as they require large-scale operations with kernel matrices for training and inferen...
Spectral-sparse signals are those sparse in the Fourier domain and are very common in wireless communications, radar, sonar, medical imaging and other applications. Their recovery from noisy, limited ...
A cut of a hypergraph is a partition of its vertex set into two parts, and the size of the cut is the number of edges which have nonempty intersection with each of the two parts. A classical result of...
This talk considers the distributed sparse identification problem over wireless sensor networks such that all sensors cooperatively estimate the unknown sparse parameter vector of stochastic dynamic s...
In the era of big data, many sparse linear discriminant analysis methods have been proposed for the classification and variable selection of the high-dimensional data. In order to solve the multiclass...

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