[PDF][PDF] Transformer embeddings of irregularly spaced events and their participants
ABSTRACT The neural Hawkes process (Mei & Eisner, 2017) is a generative model of
irregularly spaced sequences of discrete events. To handle complex domains with many …
irregularly spaced sequences of discrete events. To handle complex domains with many …
Noise-contrastive estimation for multivariate point processes
The log-likelihood of a generative model often involves both positive and negative terms. For
a temporal multivariate point process, the negative term sums over all the possible event …
a temporal multivariate point process, the negative term sums over all the possible event …
Neural Datalog through time: Informed temporal modeling via logical specification
Learning how to predict future events from patterns of past events is difficult when the set of
possible event types is large. Training an unrestricted neural model might overfit to spurious …
possible event types is large. Training an unrestricted neural model might overfit to spurious …
Cardinality-regularized hawkes-granger model
We propose a new sparse Granger-causal learning framework for temporal event data. We
focus on a specific class of point processes called the Hawkes process. We begin by …
focus on a specific class of point processes called the Hawkes process. We begin by …
Multi-aspect mining of complex sensor sequences
T Honda, Y Matsubara, R Neyama… - … Conference on Data …, 2019 - ieeexplore.ieee.org
In recent years, a massive amount of time-stamped sensor data has been generated and
collected by many Internet of Things (IoT) applications, such as advanced automobiles and …
collected by many Internet of Things (IoT) applications, such as advanced automobiles and …
Mining bursty groups from interaction data
Empirical studies and theoretical models both highlight burstinessas a common temporal
pattern in online behavior. A key driver for burstiness is the self-exciting nature of online …
pattern in online behavior. A key driver for burstiness is the self-exciting nature of online …
Poppy: a point process toolbox based on PyTorch
H Xu - arXiv preprint arXiv:1810.10122, 2018 - arxiv.org
PoPPy is a Point Process toolbox based on PyTorch, which achieves flexible designing and
efficient learning of point process models. It can be used for interpretable sequential data …
efficient learning of point process models. It can be used for interpretable sequential data …
[图书][B] Applications and Properties of Point Processes
CJ Kresin - 2023 - search.proquest.com
This dissertation discusses the properties of point process models for epidemic diseases
and other clustered phenomena. We present (1) a novel computationally efficient estimator …
and other clustered phenomena. We present (1) a novel computationally efficient estimator …
TensorMode Algorithm for Network Embedding in Dynamic Environments
C Connell, Y Wang - 2021 International Conference on Data …, 2021 - ieeexplore.ieee.org
Network embeddings into metric spaces provide convenient representations of learned
relationships between features attached to nodes and links. Embeddings into low …
relationships between features attached to nodes and links. Embeddings into low …
Neural Probabilistic Methods for Event Sequence Modeling
H Mei - 2021 - jscholarship.library.jhu.edu
This thesis focuses on modeling event sequences, namely, sequences of discrete events in
continuous time. We build a family of generative probabilistic models that is able to reason …
continuous time. We build a family of generative probabilistic models that is able to reason …