Abstract: Despite the significant success of deep learning in computer vision tasks, cross-domain tasks still present a challenge in which the model’s performance will degrade when the training set ...

Single-cell RNA-seq AI analysis has become the default way to make sense of the millions of expression measurements a single experiment can now generate. Turning raw sequencing counts into ...

description The paper proposes DeepEISNN, a normalization-free learning framework based on cortical excitatory-inhibitory (E-I) circuits. By implementing E-I Init and E-I Prop, it achieves stable ...

AI training and inference are all about running data through models — typically to make some kind of decision. But the paths that the calculations take aren’t always straightforward, and as a model ...

Abstract: The fast execution speed and energy efficiency of analog hardware have made them a strong contender for deploying deep learning models at the edge. However, there are concerns about the ...

PyTorch implementations of the deep residual networks published in "Deep Residual Learning for Image Recognition" by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. The images were preprocessed by ...

It is well recognized that batch effect in single-cell RNA sequencing (scRNA-seq) data remains a big challenge when integrating different datasets. Here, we proposed deepMNN, a novel deep ...