arXiv Machine Learning
Jul 3
Efficient Temporal Point Processes via Monotone Alternating Splines
arXiv:2607. 01752v1 Announce Type: new Abstract: Temporal point processes (TPPs) have widespread applications across various domains.
Smooth Neural Point Processes via B-Splines
arXiv:2607. 21098v1 Announce Type: new Abstract: Temporal point processes (TPPs) provide a general and flexible framework for modeling sequences of events in continuous time.
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arXiv Machine Learning
Jun 5
Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches
arXiv:2501. 14291v3 Announce Type: replace Abstract: Temporal point processes (TPPs) are stochastic process models used to characterize event sequences occurring in continuous time.
arXiv Machine Learning
Jul 31
Enhancing Irregular Time Series Forecasting with Continuous-Time Modeling Framework
arXiv:2607. 28035v1 Announce Type: new Abstract: Irregular multivariate time series are widely encountered in applications such as healthcare monitoring, human activity recognition, and environmental sensing.
arXiv Machine Learning
Jul 7
Advances in Neural Controlled Differential Equations
arXiv:2607. 05280v1 Announce Type: new Abstract: Many real-world systems evolve continuously, yet most machine learning models interpret time series as discrete sequences.
arXiv Machine Learning
Jun 9
In-Context Learning of Temporal Point Processes with Foundation Inference Models
arXiv:2509. 24762v3 Announce Type: replace Abstract: Modeling event sequences of multiple event types with marked temporal point processes (MTPPs) provides a principled way to uncover governing dynamical rules and predict future events.
arXiv Machine Learning
Jul 9
From Jumps to Signatures: a Generative Method for Temporal Point Processes
arXiv:2607. 06652v1 Announce Type: new Abstract: Rough path signatures are a universal feature map for continuous paths and, via the expected signature, characterise path distributions.
arXiv Machine Learning
Jun 5
MSTN: A Lightweight and Fast Model for General TimeSeries Analysis
arXiv:2511. 20577v5 Announce Type: replace Abstract: Real-world time series often exhibit strong non-stationarity, complex nonlinear dynamics, and behavior expressed across multiple temporal scales, from rapid local fluctuations to slow-evolving long-range trends.
arXiv Machine Learning
Jul 14
NeuroMem-FHP: A Likelihood-Free Deep Learning Framework for Parameter Estimation of Fractional Hawkes Process
arXiv:2607. 11177v1 Announce Type: new Abstract: In this paper, we propose deep learning based NeuroMem-FHP framework for estimating the parameters of the fractional Hawkes process (FHP), a self-exciting point process that captures long-range dependence through a fractional Mittag-Leffler excitation kernel.
arXiv Machine Learning
2d ago
L-FNO: Lorentzian Fourier Neural Operator for Stochastic Event Dynamics
arXiv:2608. 13562v1 Announce Type: new Abstract: Modern operational systems face uncertainty even in routine conditions, where rare, bursty, and self-exciting events emerge from both exogenous covariates and endogenous event dynamics.
arXiv Machine Learning
Jun 2
Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP
arXiv:2605. 27527v2 Announce Type: replace-cross Abstract: Astrophysical observations from Earth are subject to weather, environmental, and scientific constraints that lead to sparse, irregular light curves.
arXiv AI
Jun 11
Multi-Rate Mixture of Experts for Accelerating Liquid Neural Network Training
arXiv:2606. 12240v1 Announce Type: cross Abstract: Multivariate time-series data often exhibit complex temporal dependencies, irregular sampling, and heterogeneous dynamics across multiple time scales, making accurate sequence modeling particularly challenging.
arXiv Machine Learning
Jul 28
Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions
arXiv:2506. 05678v3 Announce Type: replace Abstract: The evolution of sequence modeling architectures, from recurrent neural networks and convolutional models to Transformers and structured state-space models, reflects ongoing efforts to address the diverse temporal dependencies inherent in sequential data.