output
20142026
most citedConcepts and status of Chinese space gravitational wave detection projects

336 citations

Showing 2021Show all

6 papers · 1 filter

cs.LG2021★ 68 cited

HampDTI: a heterogeneous graph automatic meta-path learning method for drug-target interaction prediction

Hongzhun Wang, Feng Huang, Wen Zhang

Motivation: Identifying drug-target interactions (DTIs) is a key step in drug repositioning. In recent years, the accumulation of a large number of genomics and pharmacology data h…

astro-ph.IM2021★ 336 cited

Concepts and status of Chinese space gravitational wave detection projects

Yungui Gong, Jun Luo, Bin Wang

Gravitational wave (GW) detection in space probes GW spectrum that is inaccessible from the Earth. In addition to LISA project led by European Space Agency, and the DECIGO detector…

cs.LG2021★ 26 cited

Tight Compression: Compressing CNN Through Fine-Grained Pruning and Weight Permutation for Efficient Implementation

Xizi Chen, Jingyang Zhu, Jingbo Jiang +1

The unstructured sparsity after pruning poses a challenge to the efficient implementation of deep learning models in existing regular architectures like systolic arrays. On the oth…

cond-mat.stat-mech2021★ 8 cited

Master equation approach to the stochastic accumulation dynamics of bacterial cell cycle

Liang Luo, Yang Bai, Xiongfei Fu

The mechanism of bacterial cell size control has been a mystery for decades, which involves the well-coordinated growth and division in the cell cycle. The revolutionary modern tec…

cs.CV2021★ 2 cited

Accelerating Large Kernel Convolutions with Nested Winograd Transformation.pdf

Jingbo Jiang, Xizi Chen, Chi-Ying Tsui

Recent literature has shown that convolutional neural networks (CNNs) with large kernels outperform vision transformers (ViTs) and CNNs with stacked small kernels in many computer…

cs.LG2021★ 58 cited

A Bayesian Federated Learning Framework with Online Laplace Approximation

Liangxi Liu, Xi Jiang, Feng Zheng +4

Federated learning (FL) allows multiple clients to collaboratively learn a globally shared model through cycles of model aggregation and local model training, without the need to s…