Point Processes
134 papers with code • 0 benchmarks • 2 datasets
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Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families
Maximum likelihood and maximum a posteriori estimates in a reparameterisation of the final layer of the intensity function can be obtained by solving a (strongly) convex optimisation problem using projected gradient descent.
Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes
A primary objective is to generate a distribution-free joint prediction region for the arrival time and mark, with a finite-sample marginal coverage guarantee.
Probabilistic Modeling for Sequences of Sets in Continuous-Time
In this work, we develop a general framework for modeling set-valued data in continuous-time, compatible with any intensity-based recurrent neural point process model.
Stopping Methods for Technology Assisted Reviews based on Point Processes
Technology Assisted Review (TAR), which aims to reduce the effort required to screen collections of documents for relevance, is used to develop systematic reviews of medical evidence and identify documents that must be disclosed in response to legal proceedings.
Prompt-augmented Temporal Point Process for Streaming Event Sequence
Neural Temporal Point Processes (TPPs) are the prevalent paradigm for modeling continuous-time event sequences, such as user activities on the web and financial transactions.
Deep Representation Learning for Prediction of Temporal Event Sets in the Continuous Time Domain
To the best of our knowledge, this is the first work that solves the problem of predicting event set intensities in the continuous time domain by using TPPs.
Intensity-free Integral-based Learning of Marked Temporal Point Processes
In the marked temporal point processes (MTPP), a core problem is to parameterize the conditional joint PDF (probability distribution function) $p^*(m, t)$ for inter-event time $t$ and mark $m$, conditioned on the history.
Rumor Detection with Diverse Counterfactual Evidence
Our intuition is to exploit the diverse counterfactual evidence of an event graph to serve as multi-view interpretations, which are further aggregated for robust rumor detection results.
EasyTPP: Towards Open Benchmarking Temporal Point Processes
In this paper, we present EasyTPP, the first central repository of research assets (e. g., data, models, evaluation programs, documentations) in the area of event sequence modeling.
On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data
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.