Point Processes
135 papers with code • 0 benchmarks • 2 datasets
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Libraries
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BillSum
Latest papers
On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data
tanguybosser/ntpp-tmlr2023
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To bridge this gap, we present a comprehensive large-scale experimental study that systematically evaluates the predictive accuracy of state-of-the-art neural TPP models.
On sampling determinantal and Pfaffian point processes on a quantum computer
Most applications require sampling from a DPP, and given their quantum origin, it is natural to wonder whether sampling a DPP on a quantum computer is easier than on a classical one.
Spatio-temporal Diffusion Point Processes
facebookresearch/neural_stpp
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To enhance the learning of each step, an elaborated spatio-temporal co-attention module is proposed to capture the interdependence between the event time and space adaptively.
Variational Inference for Neyman-Scott Processes
hongchengkuan/inclusive_vi_nsps
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Neyman-Scott processes (NSPs) have been applied across a range of fields to model points or temporal events with a hierarchy of clusters.
Transformer-Based Neural Marked Spatio Temporal Point Process Model for Football Match Events Analysis
calvinyeungck/football-match-event-forecast
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However, most sports sequential events modeling methods and performance metrics approaches could be incomprehensive in dealing with such large-scale spatiotemporal data (in particular, temporal process), thereby necessitating a more comprehensive spatiotemporal model and a holistic performance metric.
Compositional Exemplars for In-context Learning
hkunlp/icl-ceil
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The performance of ICL is highly dominated by the quality of the selected in-context examples.
Forecasting the 2016-2017 Central Apennines Earthquake Sequence with a Neural Point Process
ss15859/neural-point-process
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We investigate whether these flexible point process models can be applied to short-term seismicity forecasting by extending an existing temporal neural model to the magnitude domain and we show how this model can forecast earthquakes above a target magnitude threshold.
Who Should I Engage with At What Time? A Missing Event Aware Temporal Graph Neural Network
In real-world applications, events are not always observable, and estimating event time is as important as predicting future events.
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processes
However, there are more non-neighbour nodes in the whole graph, which provide diverse and useful information for the representation update.
Beyond Hawkes: Neural Multi-event Forecasting on Spatio-temporal Point Processes
negar-erfanian/neural-spatio-temporal-probabilistic-transformers
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Predicting discrete events in time and space has many scientific applications, such as predicting hazardous earthquakes and outbreaks of infectious diseases.
