Multiple-response dynamic graph regressionGraph embeddingUpdate timeAffine embeddingsSubsampled randomized hadamard transformCountSketchIn themultiple-response dynamic graph regressionproblem, given an脳d\\docum
These embedding vectors will be classified by a simple software readout layer (with 102 floating-point weights) optimized by linear regression at low hardware and energy cost (see Methods for the implementation and training of the readout layer and Supplementary Table 3 for the cost of the ...
They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the ...
(A) Bayesian network representing the joint distribution of y and its parents; (B) factor graph for a logistic regression for the conditional distribution of y given its parents. Let us assume that all variables are binary. Given a separate function fi(y,xi) for each binary variable xi, ...
sometimes with a regression line added. There are several reasons why regression is not appropriate for this task. An alternative method described uses the deviations (prediction minus observation) plotted against the observations. From this graph of deviations the bias and precision of the model can...
We propose a multimodal deep learning architecture, called GPDRP for DRP. The DRP problem is formulated as a regression task, wherein a drug-cell line pair serves as the input and a continuous measurement of the response value LN IC50 of that pair serves as the output. Molecular graphs are...
Accurate and real-time traffic passenger flows forecasting at transportation hubs, such as subway/bus stations, is a practical application and of great significance for urban traffic planning, control, guidance, etc. Recently deep learning based methods are promised to learn the spatial-temporal featur...
Textual Graphs. The textual graph is converted into a natural language format, resulting in a list of nodes and edges, akin to a CSV file format. It is important to note that while multiple methods exist for textualizing a graph, our focus is not on identifying the optimal solution. Instea...
addresses the heterophily in the protein-ligand complex graph. The second part is the pooling block, which is described in [2] and includes the pairwise interactive pooling (PiPool) for leveraging long-range interactions and the output pooling layer for predicting the protein-ligand binding ...
GIPA: General Information Propagation Algorithm for Graph Learning,Qinkai Zheng, Houyi Li, Peng Zhang, Zhixiong Yang, Guowei Zhang, Xintan Zeng, Yongchao Liu Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification, NAACL'21,Xiaochen Hou, Peng Qi, Guangtao Wa...