Papers Point Processes
“Point Processes” 태그가 달린 논문 598편 · 필터 해제
Existence-Field Diffusion Model for Spatial Point Processes with Variable Cardinality
We study generative modeling of spatial point processes (SPP), where both the number of points and their spatial configuration are governed by a joint distribution. While diffusion models have achieved strong performance…
Point ProcessesSmooth Neural Point Processes via B-Splines
Temporal point processes (TPPs) provide a general and flexible framework for modeling sequences of events in continuous time. Neural networks have been successfully employed to model TPPs in a highly expressive and data-…
Computational EfficiencyPoint ProcessesFast determinantal sampling on general spaces and diffusion geometry
Determinantal point processes have recently emerged as a kernel-based alternative to standard independent sampling for constructing efficient minibatches, coresets, and other compact representations of large-scale datase…
Point ProcessesFrom Jumps to Signatures: a Generative Method for Temporal Point Processes
Rough path signatures are a universal feature map for continuous paths and, via the expected signature, characterise path distributions. These guarantees do not directly extend to cadlag paths of Temporal Point Processes…
Point ProcessesEfficient Temporal Point Processes via Monotone Alternating Splines
Temporal point processes (TPPs) have widespread applications across various domains. Compared to modeling the conditional intensity of a TPP, modeling its cumulative conditional intensity function (CCIF) improves computa…
Computational EfficiencyPoint ProcessesSeahorse: A Unified Benchmarking Framework for Spatiotemporal Event Modeling
Spatiotemporal point processes (STPPs) model event data in continuous time and space, with applications in mobility, epidemiology, and public safety. Recent neural STPPs span expressive intensity models, conditional dens…
Point ProcessesLatent Block-Diffusion Temporal Point Processes: A Semi-Autoregressive Framework for Asynchronous Event Sequence Generation
Modeling and sampling from the underlying distribution of asynchronous event sequences are crucial in various real-world applications, including social networks, medical diagnosis, and financial transactions. Existing au…
Medical DiagnosisPoint ProcessesSpectral Certificates and Projection-DPP Rounding for Determinantal MAP Selection
Selecting a fixed-size subset that maximizes the determinant of a positive semidefinite kernel is the MAP problem for a size-constrained determinantal point process and the classical maximum-entropy sampling problem. Alt…
Point ProcessesGLIDE: Graph-guided Leap Inference for Diffusion Estimation of Spatio-Temporal Point Processes
Spatio-temporal point processes (STPPs) provide a principled framework for modeling asynchronous events in continuous time and space. Recent diffusion-based approaches offer a flexible alternative to deterministic predic…
Point ProcessesFrom DPPs to $k$-DPPs: identifiability analysis via spectral decomposition
We study the geometry of determinantal point processes (DPPs) through the spectral decomposition $L=UΛU^{\top}$. The spectrum $Λ$ governs the cardinality distribution via elementary symmetric polynomials, while the eigen…
Point ProcessesStructured Neural Marked Point Processes for Interpretable Event Interaction Modeling
Multi-class event streams arise in numerous real-world applications, where uncovering structured, interpretable inter-event relationships, together with accurate prediction, remains a central challenge. Existing neural p…
Point ProcessesState-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
Determinantal point processes (DPPs) have emerged as a kernelized alternative to vanilla independent sampling for generating efficient minibatches, coresets and other parsimonious representations of large-scale datasets.…
Point ProcessesSurF: A Generative Model for Multivariate Irregular Time Series Forecasting
Irregularly sampled multivariate event streams remain a difficult modality for generative modeling: tokenization-based approaches break down when inter-event intervals vary by orders of magnitude. We (i) propose \textbf{…
Time Series ForecastingPoint ProcessesTowards Visually Grounded Multimodal Summarization via Cross-Modal Transformer and Gated Attention
Multimodal summarization requires models to jointly understand textual and visual inputs to generate concise, semantically coherent summaries. Existing methods often inject shallow visual features into deep language mode…
Text SummarizationPoint ProcessesEfficient Conditioning Why Pseudo Observation Batch Bayesian Optimization Works When It Does not
Constant Liar (CL), Kriging Believer (KB), and fantasy models are widely used for batch selection in parallel Bayesian Optimization, yet a unified theory explaining their effectiveness and conditions under which they fai…
Gaussian ProcessesPoint ProcessesOn Observation Time for Recovering Latent Hawkes Networks
Dynamics of interacting systems in engineering, society, and nature often evolve over latent networks that govern which entities can interact. We study the problem of inferring these networks from event-based observation…
Point ProcessesThe Bayesian Reflex: Online Learning as the Autonomic Nervous System of Modern and Future AI
This chapter introduces the Bayesian reflex -- an analogy with the autonomic nervous system -- as a unifying framework for online learning in AI. Bayesian online algorithms automatically maintain equilibrium in dynamic e…
Gaussian ProcessesPoint ProcessesOn two ways to use determinantal point processes for Monte Carlo integration
The standard Monte Carlo estimator $\widehat{I}_N^{\mathrm{MC}}$ of $\int fdω$ relies on independent samples from $ω$ and has variance of order $1/N$. Replacing the samples with a determinantal point process (DPP), a rep…
Point ProcessesEVIL: Evolving Interpretable Algorithms for Zero-Shot Inference on Event Sequences and Time Series with LLMs
We introduce EVIL (\textbf{EV}olving \textbf{I}nterpretable algorithms with \textbf{L}LMs), an approach that uses LLM-guided evolutionary search to discover simple, interpretable algorithms for dynamical systems inferenc…
Point ProcessesMassively Parallel Exact Inference for Hawkes Processes
Multivariate Hawkes processes are a widely used class of self-exciting point processes, but maximum likelihood estimation naively scales as $O(N^2)$ in the number of events. The canonical linear exponential Hawkes proces…
Point Processes