WebAug 28, 2024 · We propose a graph-state LSTM model, which uses a parallel state to model each word, recurrently enriching state values via message passing. Compared … WebAug 28, 2024 · The current state-of-the-art method splits the input graph into two DAGs, adopting a DAG-structured LSTM for each. Though being able to model rich linguistic knowledge by leveraging graph edges, important information can …
Traffic forecasting using graph neural networks and LSTM …
WebLSTM disregard the internal temporal correlation and result in limited performance. Graph structures can effectively use nodes and edges to represent LBA (delta) and access sequence, and can mine intrinsic access patterns beyond chronological order in hy-brid storage systems like relational databases. Therefore, to WebMar 9, 2024 · In recent years, complex multi-stage cyberattacks have become more common, for which audit log data are a good source of information for online monitoring. However, predicting cyber threat events based on audit logs remains an open research problem. This paper explores advanced persistent threat (APT) audit log information and … chs wildcats
N-ary Relation Extraction using Graph-State LSTM - ACL …
WebMar 21, 2024 · The short-term bus passenger flow prediction of each bus line in a transit network is the basis of real-time cross-line bus dispatching, which ensures the efficient utilization of bus vehicle resources. As bus passengers transfer between different lines, to increase the accuracy of prediction, we integrate graph features into the recurrent neural … WebIn quantum computing, a graph state is a special type of multi- qubit state that can be represented by a graph. Each qubit is represented by a vertex of the graph, and there is … WebN-ray Relation Extraction using Graph State LSTM This repository corresponds to code for " N-ary Relation Extraction using Graph State LSTM ", which has been accpeted by … des directorate of economics and statistics