arXiv Machine Learning July 13, 2026 By Aaron Zweig, Mingxuan Zhang, David A. Knowles, Elham Azizi

Learning Lineage-guided Geodesics with Finsler Geometry

Read the original on arXiv Machine Learning →

arXiv:2603. 16708v2 Announce Type: replace Abstract: Trajectory inference investigates how to interpolate paths between observed timepoints of dynamical systems, such as temporally resolved population distributions, with the goal of inferring trajectories at unseen times and better understanding system dynamics.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 9

Riemannian-Manifold Steering: Geometry-Aware Generative Autoencoders for Label-Free Steering

arXiv:2605. 24942v2 Announce Type: replace-cross Abstract: Steering a language model - intervening on its internal activations to change downstream behaviour - has recently expanded beyond linear interpolation to nonlinear methods such as angular and kernelized steering, which define intervention transformations without learning an explicit geometry over paths in activation space.

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arXiv Machine Learning

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Learning the Geometry of Data: A Mathematical Review of Shape Space Analysis

arXiv:2606. 17022v1 Announce Type: cross Abstract: A central objective of machine learning is to identify structure and patterns in data.

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arXiv AI

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Geodesics with Unified Tangent-constrained Priors and Curvature Regularization

arXiv:2606. 00139v1 Announce Type: cross Abstract: Curvature-penalized geodesic models have proven their effectiveness in image segmentation by computing globally optimal curves.

By Chong Di, Li Liu, Jinglin Zhang, Zhenjiang Li, Da Chen, Laurent D. Cohen

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arXiv Machine Learning

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Riemannian MeanFlow for One-Step Generation on Manifolds

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arXiv Machine Learning

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Physically-Relevant Information Learning in High-Dimensional Time-Derivatives Spaces

arXiv:2607. 05127v1 Announce Type: cross Abstract: Understanding the physics of many-body complex dynamical systems is typically non-trivial.

By Domiziano Doria, Matteo Becchi, Giovanni M. Pavan

arXiv AI

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FlowPath: Learning Data-Driven Manifolds with Invertible Flows for Robust Irregularly-sampled Time Series Classification

arXiv:2511. 10841v3 Announce Type: replace-cross Abstract: Modeling continuous-time dynamics from sparse and irregularly-sampled time series remains a fundamental challenge.

By YongKyung Oh, Dong-Young Lim, Sungil Kim

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