A paradigm for diagnostic neural network systems that emphasizes informative data representation and encoding and uses generic preprocessing techniques to extract knowledge from database records is discussed. The proposed diagnostic system differs from other approaches to automatic knowledge extraction in the ...
大家好,今天和大家分享的是ICML19的论文 《Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance》,这篇文章介绍了一种用图神经网络建模音乐乐符关系的方法来生成富有表现力的…
Fig. 1: Data augmentation for learning the potential energy surface (PES). The red line denotes a 2D representation of the continuous PES of materials. The blue line illustrates the effective PES, which describes the energy of a relaxed structure for a given unrelaxed input structure. Data aug...
In the paper some new methods, which try to build information about the problem at hand into the representation, are proposed. It is shown that they are less sensitive to input data errors. 展开 关键词: Artificial neural networks Input data sensitivity Distributed data representation Simulation ...
The network input comprises the real and imaginary parts of the time-frequency domain signal. In contrast to high- level features extraction, such a representation retains all the information of the signal and allows the network to implicitly extract informative features for localization, which potenti...
pool) that significantly reduce this source of main memory pressure on GPUs. We find that a feature map typically has two uses that are spread far apart temporally. Our key approach is to store an encoded representation of feature maps for this temporal gap and decode this data for use in ...
The Flexible Hub theory provides a network account of how large-scale cognitive control networks implement flexible cognition by updating task rule representations15,16. While the Flexible Hub theory primarily focuses on the importance of flexible rule updating for complex task performance, it does not...
Algorithmic techniques are not directly incorporated into the training of a neural network, but rather take the form of more involved pre-processing of data. Automatic techniques employ machine learning algorithms to discover a data representation. Hence, they have an intrinsic dependence on the ...
30、Topology optimization based graph convolutional network. In IJCAI, 2019. 31、Graph structure learning for robust graph neural networks. In KDD, 2020. 32、 Robust graph representation learning via neural sparsification. In ICML, 2020. 33、Learning to drop: Robust graph neural network via topologi...
Representation learning (表示学习/表征学习) 5. You now have a model with 2 hidden layers. The values for an input data point are shown inside the input nodes. The weights are shown on the edges/lines. What prediction would this model make on this data point?