Papers Point Processes
“Point Processes” 태그가 달린 논문 599편 · 필터 해제
EventFlow: Forecasting Continuous-Time Event Data with Flow Matching
Continuous-time event sequences, in which events occur at irregular intervals, are ubiquitous across a wide range of industrial and scientific domains. The contemporary modeling paradigm is to treat such data as realizat…
Point ProcessesFederated Neural Nonparametric Point Processes
Temporal point processes (TPPs) are effective for modeling event occurrences over time, but they struggle with sparse and uncertain events in federated systems, where privacy is a major concern. To address this, we propo…
Point ProcessesIs Score Matching Suitable for Estimating Point Processes?
Score matching estimators have gained widespread attention in recent years partly because they are free from calculating the integral of normalizing constant, thereby addressing the computational challenges in maximum li…
Point ProcessesTPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Models
Temporal point processes (TPPs) are widely used to model the timing and occurrence of events in domains such as social networks, transportation systems, and e-commerce. In this paper, we introduce TPP-LLM, a novel framew…
Computational Efficiencyparameter-efficient fine-tuningPoint ProcessesEarthquakeNPP: Benchmark Datasets for Earthquake Forecasting with Neural Point Processes
Classical point process models, such as the epidemic-type aftershock sequence (ETAS) model, have been widely used for forecasting the event times and locations of earthquakes for decades. Recent advances have led to Neur…
BenchmarkingDataset GenerationPoint ProcessesDecomposable Transformer Point Processes
The standard paradigm of modeling marked point processes is by parameterizing the intensity function using an attention-based (Transformer-style) architecture. Despite the flexibility of these methods, their inference is…
Point ProcessesOn Your Mark, Get Set, Predict! Modeling Continuous-Time Dynamics of Cascades for Information Popularity Prediction
Information popularity prediction is important yet challenging in various domains, including viral marketing and news recommendations. The key to accurately predicting information popularity lies in subtly modeling the u…
MarketingPoint ProcessesSMART-RAG: Selection using Determinantal Matrices for Augmented Retrieval
Retrieval-Augmented Generation (RAG) has greatly improved large language models (LLMs) by enabling them to generate accurate, contextually grounded responses through the integration of external information. However, conv…
DiversityPoint ProcessesQuestion AnsweringRAG+2A Model-Free Method to Quantify Memory Utilization in Neural Point Processes
Quantifying the predictive capacity of a neural system, intended as the capability to store information and actively use it for dynamic system evolution, is a key component of neural information processing. Information s…
Point ProcessesDeTPP: Leveraging Object Detection for Robust Long-Horizon Event Prediction
Long-horizon event forecasting is critical across various domains, including retail, finance, healthcare, and social networks. Traditional methods, such as Marked Temporal Point Processes (MTPP), often rely on autoregres…
DiversityPoint ProcessesTime Series ForecastingSpectrum Sharing Between Low Earth Orbit Satellite and Terrestrial Networks: A Stochastic Geometry Perspective Analysis
Low Earth orbit (LEO) satellite networks with mega constellations have the potential to provide 5G and beyond services ubiquitously. However, these networks may introduce mutual interference to both satellite and terrest…
Point ProcessesAn Adaptive Importance Sampling for Locally Stable Point Processes
The problem of finding the expected value of a statistic of a locally stable point process in a bounded region is addressed. We propose an adaptive importance sampling for solving the problem. In our proposal, we restric…
Point ProcessesAn unbounded intensity model for point processes
We develop a model for point processes on the real line, where the intensity can be locally unbounded without inducing an explosion. In contrast to an orderly point process, for which the probability of observing more th…
modelPoint ProcessesStochastic Geometry Analysis of RIS-Assisted Cellular Networks with Reflective Intelligent Surfaces on Roads
Reconfigurable intelligent surfaces (RISs) provide alternative routes for reflected signals to network users, offering numerous applications. This paper explores an innovative approach of strategically deploying RISs alo…
Point ProcessesA Family of Distributions of Random Subsets for Controlling Positive and Negative Dependence
Positive and negative dependence are fundamental concepts that characterize the attractive and repulsive behavior of random subsets. Although some probabilistic models are known to exhibit positive or negative dependence…
Point ProcessesDistributionally Robust Optimization as a Scalable Framework to Characterize Extreme Value Distributions
The goal of this paper is to develop distributionally robust optimization (DRO) estimators, specifically for multidimensional Extreme Value Theory (EVT) statistics. EVT supports using semi-parametric models called max-st…
Point ProcessesvalidTransFeat-TPP: An Interpretable Deep Covariate Temporal Point Processes
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 factor, other contextual data, termed co…
Feature ImportancePoint ProcessesHave ASkotch: A Neat Solution for Large-scale Kernel Ridge Regression
Kernel ridge regression (KRR) is a fundamental computational tool, appearing in problems that range from computational chemistry to health analytics, with a particular interest due to its starring role in Gaussian proces…
Computational chemistryPoint ProcessesregressionMamba Hawkes Process
Irregular and asynchronous event sequences are prevalent in many domains, such as social media, finance, and healthcare. Traditional temporal point processes (TPPs), like Hawkes processes, often struggle to model mutual …
Computational EfficiencyMambaPoint ProcessesState Space ModelsExact Bayesian Gaussian Cox Processes Using Random Integral
A Gaussian Cox process is a popular model for point process data, in which the intensity function is a transformation of a Gaussian process. Posterior inference of this intensity function involves an intractable integral…
Data AugmentationPoint Processes