Papers Point Processes
“Point Processes” 태그가 달린 논문 599편 · 필터 해제
Diverse Mini-Batch Selection in Reinforcement Learning for Efficient Chemical Exploration in de novo Drug Design
In many real-world applications, evaluating the goodness of instances is often costly and time-consuming, e.g., human feedback and physics simulations, in contrast to proposing new instances. In particular, this is even …
Drug DesignDrug DiscoveryPoint ProcessesUrban RIS-Assisted HAP Networks: Performance Analysis Using Stochastic Geometry
This paper studies a high-altitude platform (HAP) network supported by reconfigurable intelligent surfaces (RISs). The practical irregular placement of HAPs and RISs is modeled using homogeneous Poisson point processes, …
Point ProcessesUncovering Social Network Activity Using Joint User and Topic Interaction
The emergence of online social platforms, such as social networks and social media, has drastically affected the way people apprehend the information flows to which they are exposed. In such platforms, various informatio…
Point ProcessesTurbocharging Gaussian Process Inference with Approximate Sketch-and-Project
Gaussian processes (GPs) play an essential role in biostatistics, scientific machine learning, and Bayesian optimization for their ability to provide probabilistic predictions and model uncertainty. However, GP inference…
Bayesian OptimizationGaussian ProcessesPoint ProcessesEvaluating Uncertainty in Deep Gaussian Processes
Reliable uncertainty estimates are crucial in modern machine learning. Deep Gaussian Processes (DGPs) and Deep Sigma Point Processes (DSPPs) extend GPs hierarchically, offering promising methods for uncertainty quantific…
Gaussian ProcessesPoint ProcessesUncertainty QuantificationInterpretable Hybrid-Rule Temporal Point Processes
Temporal Point Processes (TPPs) are widely used for modeling event sequences in various medical domains, such as disease onset prediction, progression analysis, and clinical decision support. Although TPPs effectively ca…
Bayesian OptimizationMedical DiagnosisPoint ProcessesvalidFiltering through a topological lens: homology for point processes on the time-frequency plane
We introduce a very general approach to the analysis of signals from their noisy measurements from the perspective of Topological Data Analysis (TDA). While TDA has emerged as a powerful analytical tool for data with pro…
Point ProcessesTopological Data AnalysisDeep spatio-temporal point processes: Advances and new directions
Spatio-temporal point processes (STPPs) model discrete events distributed in time and space, with important applications in areas such as criminology, seismology, epidemiology, and social networks. Traditional models oft…
EpidemiologyPoint ProcessesA stochastic volatility approximation for a tick-by-tick price model with mean-field interaction
We consider a tick-by-tick model of price formation, in which buy and sell orders are modeled as self-exciting point processes (Hawkes process), similar to the one in [El Euch, Fukasawa, Rosenbaum, The microstructural fo…
Point ProcessesDynamic Graph Structure Estimation for Learning Multivariate Point Process using Spiking Neural Networks
Modeling and predicting temporal point processes (TPPs) is critical in domains such as neuroscience, epidemiology, finance, and social sciences. We introduce the Spiking Dynamic Graph Network (SDGN), a novel framework th…
Computational EfficiencyEpidemiologyPoint ProcessesLearning Structure-enhanced Temporal Point Processes with Gromov-Wasserstein Regularization
Real-world event sequences are often generated by different temporal point processes (TPPs) and thus have clustering structures. Nonetheless, in the modeling and prediction of event sequences, most existing TPPs ignore t…
ClusteringPoint ProcessesWhenever, Wherever: Towards Orchestrating Crowd Simulations with Spatio-Temporal Spawn Dynamics
Realistic crowd simulations are essential for immersive virtual environments, relying on both individual behaviors (microscopic dynamics) and overall crowd patterns (macroscopic characteristics). While recent data-driven…
Deep Reinforcement LearningDiversityPoint ProcessesStochastic Geometry for Modeling and Analysis of Sensing and Communications: A Survey
One of the most promising technologies for next-generation wireless networks is integrated communication and sensing (ISAC). It is considered a key enabler for applications that require both enhanced communication and ac…
ArticlesISACPoint ProcessesPER-DPP Sampling Framework and Its Application in Path Planning
Autonomous navigation in intelligent mobile systems represents a core research focus within artificial intelligence-driven robotics. Contemporary path planning approaches face constraints in dynamic environmental respons…
Autonomous NavigationDiversityPoint Processesreinforcement-learning+1Differentially private synthesis of Spatial Point Processes
This paper proposes a method to generate synthetic data for spatial point patterns within the differential privacy (DP) framework. Specifically, we define a differentially private Poisson point synthesizer (PPS) and Cox …
Point ProcessesPrediction of Clinical Complication Onset using Neural Point Processes
Predicting medical events in advance within critical care settings is paramount for patient outcomes and resource management. Utilizing predictive models, healthcare providers can anticipate issues such as cardiac arrest…
ManagementPoint ProcessesPredictionRespiratory FailureNeural Spatiotemporal Point Processes: Trends and Challenges
Spatiotemporal point processes (STPPs) are probabilistic models for events occurring in continuous space and time. Real-world event data often exhibit intricate dependencies and heterogeneous dynamics. By incorporating m…
Point ProcessesNegative Dependence as a toolbox for machine learning : review and new developments
Negative dependence is becoming a key driver in advancing learning capabilities beyond the limits of traditional independence. Recent developments have evidenced support towards negatively dependent systems as a learning…
Dimensionality ReductionPoint ProcessesLanguage-TPP: Integrating Temporal Point Processes with Language Models for Event Analysis
Temporal Point Processes (TPPs) have been widely used for event sequence modeling, but they often struggle to incorporate rich textual event descriptions effectively. Conversely, while Large Language Models (LLMs) have b…
Point ProcessesType predictionLeveraging band diversity for feature selection in EO data
Hyperspectral imaging (HSI) is a powerful earth observation technology that captures and processes information across a wide spectrum of wavelengths. Hyperspectral imaging provides comprehensive and detailed spectral dat…
DiversityEarth Observationfeature selectionPoint Processes