TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data.
Lun Du, Fei Gao, Xu Chen, Ran Jia, Junshan Wang,et al; KDD (2021)
To simultaneously extract spatial and relational information from tables, we propose a novel neural network architecture, TabularNet. The spatial encoder of TabularNet utilizes the row/column-level Pooling and the Bidirectional Gated Recurrent Unit (Bi-GRU) to capture statistical information and local positional correlation, respectively. For relational information, we design a new graph construction method based on the WordNet tree and adopt a Graph Convolutional Network (GCN) based encoder that focuses on the hierarchical and paratactic relationships between cells. Our neural network architecture can be a unified neural backbone for different understanding tasks and utilized in a multitask scenario. We conduct extensive experiments on three classification tasks with two real-world spreadsheet data sets, and the results demonstrate the effectiveness of our proposed TabularNet over state-of-the-art baselines.
Automated Discovery of Interactions and Dynamics for Large Networked Dynamical Systems
Yan Zhang, Yu Guo, Zhang Zhang, Mengyuan Chen, Shuo Wang, Jiang Zhang, 2021
Based on a Bernoulli network generator and a Markov dynamics learner, this paper proposes a unified framework for Automated Interaction network and Dynamics Discovery (AIDD) on various network structures and different types of dynamics. The experiments show that AIDD can be applied on large systems with thousands of nodes. AIDD can not only infer the unknown network structure and states for hidden nodes but also can reconstruct the real gene regulatory network based on the noisy, incomplete, and being disturbed data which is closed to real situations. We further propose a new method to test data-driven models by experiments of control. We optimize a controller on the learned model, and then apply it on both the learned and the ground truth models. The results show that both of them behave similarly under the same control law, which means AIDD models have learned the real network dynamics correctly.
scCapsNet: a deep learning classifier with the capability of interpretable feature extraction, applicable for single cell RNA data analysis
Lifei Wang,Rui Nie,Zeyang Yu, Ruyue Xin, Caihong Zheng, Zhang Zhang, Jiang Zhang, Jun Cai; Nature Machine Intelligence, 2: 693-703(2020)
The scCapsNet model retains the capsule parts of CapsNet but replaces the part of convolutional neural networks with several parallel fully connected neural networks. We apply scCapsNet to scRNA-seq data. The results show that scCapsNet performs well as a classifier and also that the parallel fully connected neural networks function like feature extractors as we supposed.
A General Deep Learning Framework for Network Reconstruction and Dynamics Learning
Zhang Zhang,Yi Zhao,Jing Liu, Shuo Wang, Ruyi Tao, Ruyue Xin, Jiang Zhang; Applied Network Science, 4, 110 (2019)
A New paper is published on Nature Machine Intelligence. We propose an interpretable deep-learning architecture using capsule networks (called scCapsNet). By utilizing competitive single-cell-type recognition, the scCapsNet model is able to perform feature selection to identify groups of genes encoding different subcellular types. The RNA expression signatures, which enable subcellular-type recognition, are effectively integrated into the parameter matrices of scCapsNet. This characteristic enables the discovery of gene regulatory modules in which genes interact with each other and are closely related in function, but present distinct expression patterns.
Simple spatial scaling rules behind complex cities
Ruiqi Li,Lei Dong,Jiang Zhang, Xinran Wang, Wen-xu Wang, E.G. Stanley;Nature Communications 8, 1841 (2017)
By a simple model mainly based on spatial attraction and matching growth mechanisms, we reveal that the spatial scaling rules of these three elements are in a consistent framework, which allows us to use any single observation to infer the others. All numerical and theoretical results are consistent with empirical data from ten representative cities. In addition, our model can also provide a general explanation of the origins of the universal super- and sub-linear aggregate scaling laws and accurately predict kilometre-level socioeconomic activity. Our work opens a new avenue for uncovering the evolution of cities in terms of the interplay among urban elements, and it has a broad range of applications
随着人工智能程序AlphaGo以4∶1的大比分战胜人类围棋世界冠军李世石,机器将征服人 类的担忧正在甚嚣尘上。《走近 2050》则为我们描绘了一幅人机和谐共生、协同演化的全新场景。 在看得见的未来,人类将越来越多地沉浸于五花八门的虚拟世界以获取各式体验,与此同时,我 们将心甘情愿地将自己的注意力源源不断地输入给机器世界以促使它们进化。
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