Integration-free training for spatio-temporal multimodal covariate deep kernel point processes
In this study, we propose a novel deep spatio-temporal point process model, Deep Kernel
Mixture Point Processes (DKMPP), that incorporates multimodal covariate information …
Mixture Point Processes (DKMPP), that incorporates multimodal covariate information …
One-hot generalized linear model for switching brain state discovery
Exposing meaningful and interpretable neural interactions is critical to understanding neural
circuits. Inferred neural interactions from neural signals primarily reflect functional …
circuits. Inferred neural interactions from neural signals primarily reflect functional …
Bayesian estimation of nonlinear Hawkes processes
D Sulem, V Rivoirard, J Rousseau - Bernoulli, 2024 - projecteuclid.org
The supplementary material contains ten sections and includes proofs and additional
results, notably the proofs of Proposition 2.3, Proposition 2.5, Proposition 3.5, Corollary 3.8 …
results, notably the proofs of Proposition 2.3, Proposition 2.5, Proposition 3.5, Corollary 3.8 …
Improvements on scalable stochastic Bayesian inference methods for multivariate Hawkes process
AZ Jiang, A Rodriguez - Statistics and Computing, 2024 - Springer
Abstract Multivariate Hawkes Processes (MHPs) are a class of point processes that can
account for complex temporal dynamics among event sequences. In this work, we study the …
account for complex temporal dynamics among event sequences. In this work, we study the …
Detection of short-term temporal dependencies in hawkes processes with heterogeneous background dynamics
Many kinds of simultaneously-observed event sequences exhibit mutually exciting or
inhibiting patterns. Reliable detection of such temporal dependencies is crucial for scientific …
inhibiting patterns. Reliable detection of such temporal dependencies is crucial for scientific …
Unraveling the Dynamics of Stable and Curious Audiences in Web Systems
We propose the Burst-Induced Poisson Process (BPoP), a model designed to analyze time
series data such as feeds or search queries. BPoP can distinguish between the slowly …
series data such as feeds or search queries. BPoP can distinguish between the slowly …
Heterogeneous multi-task Gaussian Cox processes
This paper presents a novel extension of multi-task Gaussian Cox processes for modeling
multiple heterogeneous correlated tasks jointly, eg, classification and regression, via multi …
multiple heterogeneous correlated tasks jointly, eg, classification and regression, via multi …
Linear normalization attention neural Hawkes process
With the development of the Internet and the formal arrival of the era of big data, people
record, store and process data in electronic form, while the bulk of the data in the real life are …
record, store and process data in electronic form, while the bulk of the data in the real life are …
TransFeat-TPP: An Interpretable Deep Covariate Temporal Point Processes
Z Meng, B Li, X Fan, Z Li, Y Wang, F Chen… - arXiv preprint arXiv …, 2024 - arxiv.org
The classical temporal point process (TPP) constructs an intensity function by taking the
occurrence times into account. Nevertheless, occurrence time may not be the only relevant …
occurrence times into account. Nevertheless, occurrence time may not be the only relevant …
Probabilistic Fusion Framework Combining CNNs and Graphical Models for Multiresolution Satellite and UAV Image Classification
Image classification-or semantic segmentation-from input multiresolution imagery is a
demanding task. In particular, when dealing with images of the same scene collected at the …
demanding task. In particular, when dealing with images of the same scene collected at the …