Deep learning algorithms are a subset of the machine learning algorithms, which aim at
discovering multiple levels of distributed representations. Recently, numerous deep learning …

Removing the bias and variance of multicentre data has always been a challenge in large
scale digital healthcare studies, which requires the ability to integrate clinical features …

In this letter, we consider the problem of channel estimation for large intelligent metasurface
(LIM) assisted massive multiple-input multiple-output (MIMO) systems. The main challenge …

Pooled CRISPR screens with single-cell RNA sequencing readout (Perturb-seq) have
emerged as a key technique in functional genomics, but they are limited in scale by cost and …

Deep convolutional networks have become a popular tool for image generation and
restoration. Generally, their excellent performance is imputed to their ability to learn realistic …

We introduce the sparse modern Hopfield model as a sparse extension of the modern
Hopfield model. Like its dense counterpart, the sparse modern Hopfield model equips a …

Deep convolutional networks have become a popular tool for image generation and
restoration. Generally, their excellent performance is imputed to their ability to learn realistic …

Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …

Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer
time resolution. The resulting data rates require reproducible analysis pipelines that are …

Sparse coding-based anomaly detection has shown promising performance, of which the
keys are feature learning, sparse representation, and dictionary learning. In this paper, we …