The paper surveys continuous‑time (CT) machine learning, a framework for modeling temporal dynamics as continuous processes, especially useful when data are sampled irregularly or over long horizons. It introduces a unified taxonomy that groups major CT methods by their underlying mathematical formulations and shows how different architectural choices—such as vector‑field parameterization, stochasticity, memory mechanisms, and discretization—relate these families. The survey compares training algorithms, optimization strategies, failure modes, computational complexity, and benchmarks, reviews supporting software ecosystems, and outlines open challenges and future research directions.
Continuous-time (CT) machine learning has emerged as a principled framework for modeling temporal dynamics as a continuous process, particularly when observations are sampled at arbitrary time points...
arXiv:2505.22509v2 Announce Type: replace Abstract: Optimization is an important module of modern machine learning applications. Tremendous efforts have been made to accelerate optimization algorithm...
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:2607. 05280v1 Announce Type: new Abstract: Many real-world systems evolve continuously, yet most machine learning models interpret time series as discrete sequences.
The paper introduces FastCDTW, an efficient approximation for Continuous Dynamic Time Warping (CDTW). It offers two computational strategies: an exact closed‑form solution for piecewise‑linear warping functions and an unbiased Monte Carlo estimator for smooth warping functions such as those implemented by implicit neural networks. Both approaches provide gradients with respect to the warping function and the input time series, making the algorithm GPU‑friendly and suitable for integration into complex learning tasks like time‑series averaging, classification, and deep domain adaptation.
arXiv:2509. 23052v2 Announce Type: replace Abstract: We present a new meta-learning method to determine the optimal learning rate schedule for gradient descent.
The Well is a 15‑terabyte benchmark covering 16 physical simulation domains, aimed at evaluating machine learning surrogate models for complex systems. Baseline neural operator results were presented as illustrative, with minimal hyperparameter tuning and a strict 12‑hour per‑run limit. A brief hyperparameter search on the Tucker‑factorized Fourier Neural Operator, using only a single input timestep, lowered VRMSE below most published baselines on 15 of 17 tasks, showing that modest tuning can dramatically improve performance.
arXiv:2610.07383v1 Announce Type: cross Abstract: Synthetic patient data generation is a promising solution to the dual challenge of data scarcity and privacy constraints in healthcare machine learni...
arXiv:2607. 27924v1 Announce Type: new Abstract: In the physical world we inhabit, space and time are fundamentally continuous.
arXiv:2608. 20044v1 Announce Type: new Abstract: Early Classification of Time Series (ECTS) requires making accurate decisions as early as possible in inherently online and evolving environments.
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:2602. 18131v2 Announce Type: replace Abstract: Temporal Predictive Coding provides a layer-local, parallelisable mechanism for learning in recurrent systems, making it an attractive candidate for online local learning on neuromorphic and edge hardware.