This is basic implementation of our AAAI'20 paper: Huiting Hong, Hantao Guo, Yucheng Lin, Xiaoqing Yang, Zang Li, Jieping Ye. 2020. An Attention-based Graph Nerual Network for Heterogeneous Structural ...

Abstract: Structured sparsity has been proposed as an efficient way to prune the complexity of Machine Learning (ML) applications and to simplify the handling of sparse data in hardware. Accelerating ...

Whether we’re using Spotify, Amazon, Netflix or Instagram, we encounter algorithms that recommend content or products to us every day. In 2017 Netflix stated that its users discover around 80 percent ...

Extensive clinical and biomedical studies have shown that microbiome plays a prominent role in human health. Identifying potential microbe–disease associations (MDAs) can help reveal the pathological ...

The Locally Competitive Algorithm (LCA) is a biologically plausible computational architecture for sparse coding, where a signal is represented as a linear combination of elements from an ...

Abstract: This paper deals with the class imbalance problem in the context of the automatic selection of the best storage format for a sparse matrix with the aim of maximizing the performance of the ...

The ability to purify macromolecular machines in an intact, stable manner is imperative to mechanistically interrogate their function within cells and to elucidate three-dimensional (3D) structures.

Nowadays a common problem when processing data sets with the large number of covariates compared to small sample sizes (fat data sets) is to estimate the parameters associated with each covariate.

Given the drawbacks of implementing multivariate analysis for mapping multiple traits in genome-wide association study (GWAS), principal component analysis (PCA) has been widely used to generate ...

Spectral algorithms are widely applied to data clustering problems, including finding communities or partitions in graphs and networks. We propose a way of encoding sparse data using a ...