Deep learning for insider threat detection: Review, challenges and opportunities
Insider threats, as one type of the most challenging threats in cyberspace, usually cause
significant loss to organizations. While the problem of insider threat detection has been …
significant loss to organizations. While the problem of insider threat detection has been …
Neural temporal point processes: A review
Temporal point processes (TPP) are probabilistic generative models for continuous-time
event sequences. Neural TPPs combine the fundamental ideas from point process literature …
event sequences. Neural TPPs combine the fundamental ideas from point process literature …
Time2vec: Learning a vector representation of time
Time is an important feature in many applications involving events that occur synchronously
and/or asynchronously. To effectively consume time information, recent studies have …
and/or asynchronously. To effectively consume time information, recent studies have …
Transformer hawkes process
Modern data acquisition routinely produce massive amounts of event sequence data in
various domains, such as social media, healthcare, and financial markets. These data often …
various domains, such as social media, healthcare, and financial markets. These data often …
Deep reinforcement learning
SE Li - Reinforcement learning for sequential decision and …, 2023 - Springer
Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …
decision strategies. However, in many cases, it is desirable to learn directly from …
Self-attentive Hawkes process
Capturing the occurrence dynamics is crucial to predicting which type of events will happen
next and when. A common method to do this is through Hawkes processes. To enhance …
next and when. A common method to do this is through Hawkes processes. To enhance …
Intensity-free learning of temporal point processes
Temporal point processes are the dominant paradigm for modeling sequences of events
happening at irregular intervals. The standard way of learning in such models is by …
happening at irregular intervals. The standard way of learning in such models is by …
Fully neural network based model for general temporal point processes
T Omi, K Aihara - Advances in neural information …, 2019 - proceedings.neurips.cc
A temporal point process is a mathematical model for a time series of discrete events, which
covers various applications. Recently, recurrent neural network (RNN) based models have …
covers various applications. Recently, recurrent neural network (RNN) based models have …
Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates
Hawkes processes are used in statistical modeling for event clustering and causal inference,
while they also can be viewed as stochastic versions of popular compartmental models used …
while they also can be viewed as stochastic versions of popular compartmental models used …
Add and thin: Diffusion for temporal point processes
Autoregressive neural networks within the temporal point process (TPP) framework have
become the standard for modeling continuous-time event data. Even though these models …
become the standard for modeling continuous-time event data. Even though these models …