Hugging Face Trending Papers
Continuous-Time Machine Learning: A Unified Mathematical Perspective
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv AI
Sep 16
Continuous-Time Machine Learning: A Unified Mathematical Perspective
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.
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
Aug 10
Optimization as a Dynamical System: Generative Schedules from Latent ODEs
arXiv:2509. 23052v2 Announce Type: replace Abstract: We present a new meta-learning method to determine the optimal learning rate schedule for gradient descent.
arXiv Machine Learning
Jun 3
Beyond Gradient Descent: Adam for Analog Ising Machines
arXiv:2606. 03917v1 Announce Type: cross Abstract: As Moore's law reaches its limits, Ising machines offer a promising alternative computing approach for difficult optimization problems.
arXiv Machine Learning
Jun 2
MedGym:A Unified Continuous-Time Benchmark for Dynamic Medical Treatment Reinforcement Learning
arXiv:2606. 01028v1 Announce Type: new Abstract: Medical treatment recommendation poses several challenges to reinforcement learning (RL): patient physiology evolves in continuous time, measurements and interventions are performed at irregular intervals, and treatment effects vary substantially across individuals.