Papers Point Processes
“Point Processes” 태그가 달린 논문 599편 · 필터 해제
Learn2Extend: Extending sequences by retaining their statistical properties with mixture models
This paper addresses the challenge of extending general finite sequences of real numbers within a subinterval of the real line, maintaining their inherent statistical properties by employing machine learning. Our focus l…
Point ProcessesStopping 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 d…
Point ProcessesTARNon-Autoregressive Diffusion-based Temporal Point Processes for Continuous-Time Long-Term Event Prediction
Continuous-time long-term event prediction plays an important role in many application scenarios. Most existing works rely on autoregressive frameworks to predict event sequences, which suffer from error accumulation, th…
DenoisingPoint ProcessesPredictionAdd and Thin: Diffusion for Temporal Point Processes
Autoregressive neural networks within the temporal point process (TPP) framework have become the standard for modeling continuous-time event data. Even though these models can expressively capture event sequences in a on…
DenoisingDensity EstimationPoint ProcessesInteracting Diffusion Processes for Event Sequence Forecasting
Neural Temporal Point Processes (TPPs) have emerged as the primary framework for predicting sequences of events that occur at irregular time intervals, but their sequential nature can hamper performance for long-horizon …
DenoisingPoint ProcessesScore Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process with Uncertainty Quantification
Spatio-temporal point processes (STPPs) are potent mathematical tools for modeling and predicting events with both temporal and spatial features. Despite their versatility, most existing methods for learning STPPs either…
Point ProcessesUncertainty QuantificationDPP-TTS: Diversifying prosodic features of speech via determinantal point processes
With the rapid advancement in deep generative models, recent neural Text-To-Speech(TTS) models have succeeded in synthesizing human-like speech. There have been some efforts to generate speech with various prosody beyond…
DiversityPoint Processestext-to-speechText to SpeechSignal reconstruction using determinantal sampling
We study the approximation of a square-integrable function from a finite number of evaluations on a random set of nodes according to a well-chosen distribution. This is particularly relevant when the function is assumed …
Point ProcessesIntegration-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. DKMPP is an enhanced version of Deep Mixture Po…
DenoisingPoint ProcessesAutomatic Integration for Spatiotemporal Neural Point Processes
Learning continuous-time point processes is essential to many discrete event forecasting tasks. However, integration poses a major challenge, particularly for spatiotemporal point processes (STPPs), as it involves calcul…
Point ProcessesPrompt-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. In real-world applications, event data is typ…
Continual LearningPoint ProcessesAmortized Network Intervention to Steer the Excitatory Point Processes
Excitatory point processes (i.e., event flows) occurring over dynamic graphs (i.e., evolving topologies) provide a fine-grained model to capture how discrete events may spread over time and space. How to effectively stee…
Decision MakingModel-based Reinforcement LearningModel Predictive ControlPoint Processes+1Deep Representation Learning for Prediction of Temporal Event Sets in the Continuous Time Domain
Temporal Point Processes (TPP) play an important role in predicting or forecasting events. Although these problems have been studied extensively, predicting multiple simultaneously occurring events can be challenging. Fo…
Computational EfficiencyPoint ProcessesRepresentation LearningComposable Coresets for Determinant Maximization: Greedy is Almost Optimal
Given a set of $n$ vectors in $\mathbb{R}^d$, the goal of the \emph{determinant maximization} problem is to pick $k$ vectors with the maximum volume. Determinant maximization is the MAP-inference task for determinantal p…
2kPoint ProcessesCoverage Analysis of Dynamic Coordinated Beamforming for LEO Satellite Downlink Networks
In this paper, we investigate the coverage performance of downlink satellite networks employing dynamic coordinated beamforming. Our approach involves modeling the spatial arrangement of satellites and users using Poisso…
Point ProcessesEnhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference
Asynchronous time series, also known as temporal event sequences, are the basis of many applications throughout different industries. Temporal point processes(TPPs) are the standard method for modeling such data. Existin…
Point ProcessesTime SeriesTime Series ForecastingVariational InferenceStochastic Geometry Analysis of a New GSCM with Dual Visibility Regions
The geometry-based stochastic channel models (GSCM), which can describe realistic channel impulse responses, often rely on the existence of both {\em local} and {\em far} scatterers. However, their visibility from both t…
Point ProcessesRTB Formulation Using Point Process
We propose a general stochastic framework for modelling repeated auctions in the Real Time Bidding (RTB) ecosystem using point processes. The flexibility of the framework allows a variety of auction scenarios including c…
Point ProcessesDetecting Structural Shifts in Multivariate Hawkes Processes with Fréchet Statistics
This paper proposes a new approach for change point detection in multivariate Hawkes processes using Fr\'echet statistic of a network. The method splits the point process into overlapping windows, estimates kernel matric…
Change Point DetectionPoint ProcessesReinforcement Logic Rule Learning for Temporal Point Processes
We propose a framework that can incrementally expand the explanatory temporal logic rule set to explain the occurrence of temporal events. Leveraging the temporal point process modeling and learning framework, the rule c…
Point Processes