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- W4313443589 abstract "Data generated by real-world events are usually obtained by condensing events into continuous signals at specific intervals, which are handled by mechanisms that make this data, a part of a continuous time function. Past models have used a parametric function that relates chronological data with event sequences, which are elicited through conditional intensity functions in order to quantify the frequency of event occurrence. Thus, the resulting data is devoid of a relation between present and past event occurrences, which is more often than not, a specific task that has been determined based on prior knowledge. This particular limitation is overcome using point process networks in Recurrent Neural Network (RNN) to which this paper on the said subject will enumerate a theoretical distinction along with practically implemented instances of RNNs where one RNN is concerned with capturing the relationship among the event sequences obtained as a result of the processes described earlier, while another is employed to update the intensity function based on time series. Finally, in order to prove the relation among data in a regular interval and in a random event an attempt to discover any anomaly or deviation from prediction is also implemented." @default.
- W4313443589 created "2023-01-06" @default.
- W4313443589 creator A5031756851 @default.
- W4313443589 creator A5058599157 @default.
- W4313443589 date "2022-11-19" @default.
- W4313443589 modified "2023-09-30" @default.
- W4313443589 title "Artificial Cognition of Temporal Events Using Recurrent Point Process Networks" @default.
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- W4313443589 doi "https://doi.org/10.1007/978-981-19-7169-3_9" @default.
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