Copertina

Geometric Principles for Machine Learning of Dynamical Systems

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Conti, Zack Xuereb, Wagg, David J, Pepper, Nick
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Fast Surrogate Models for Adaptive Aircraft Trajectory Prediction in En route Airspace
di: Pepper, Nick, et al.
Pubblicazione: (2026)

Learning Generative Models for Climbing Aircraft from Radar Data
di: Pepper, Nick, et al.
Pubblicazione: (2023)

Impact of data for forecasting on performance of model predictive control in buildings with smart energy storage
di: Langtry, Max, et al.
Pubblicazione: (2024)

Graph-based Complexity Forecasts in UK En Route Airspace Using Relevant Aircraft Interactions
di: Henderson, Edward, et al.
Pubblicazione: (2026)

An Algebraic Representation Theorem for Linear GENEOs in Geometric Machine Learning
di: Conti, Francesco, et al.
Pubblicazione: (2026)

Adaptive Sensor Steering Strategy Using Deep Reinforcement Learning for Dynamic Data Acquisition in Digital Twins
di: Ogbodo, Collins O., et al.
Pubblicazione: (2025)

Towards Agent-based Test Support Systems: An Unsupervised Environment Design Approach
di: Ogbodo, Collins O., et al.
Pubblicazione: (2025)

Geometric Machine Learning on EEG Signals
di: Choi, Benjamin J.
Pubblicazione: (2025)

TinyTorch: Building Machine Learning Systems from First Principles
di: Reddi, Vijay Janapa
Pubblicazione: (2026)

Geometric Dictionary Learning of Dynamical Systems with Optimal Transport
di: Germain, Thibaut, et al.
Pubblicazione: (2026)

Air Traffic Controller Task Demand via Graph Neural Networks: An Interpretable Approach to Airspace Complexity
di: Henderson, Edward, et al.
Pubblicazione: (2025)

Decoding the Stressed Brain with Geometric Machine Learning
di: Koszut, Sonia, et al.
Pubblicazione: (2025)

Key Principles of Graph Machine Learning: Representation, Robustness, and Generalization
di: Abbahaddou, Yassine
Pubblicazione: (2026)

Automated Machine Learning: From Principles to Practices
di: Shen, Zhenqian, et al.
Pubblicazione: (2018)

Machine Learning Hamiltonian Dynamical Systems with Sparse and Noisy Data
di: Thapar, Vedanta, et al.
Pubblicazione: (2026)

The Data Minimization Principle in Machine Learning
di: Ganesh, Prakhar, et al.
Pubblicazione: (2024)

Geometric Learning Dynamics
di: Vanchurin, Vitaly
Pubblicazione: (2025)

An Information Theoretic Approach to Machine Unlearning
di: Foster, Jack, et al.
Pubblicazione: (2024)

FedDES: Graph-Based Dynamic Ensemble Selection for Personalized Federated Learning
di: Mueller, Brianna, et al.
Pubblicazione: (2026)

Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structures
di: Papillon, Mathilde, et al.
Pubblicazione: (2024)

FairLogue: A Toolkit for Intersectional Fairness Analysis in Clinical Machine Learning Models
di: Souligne, Nick, et al.
Pubblicazione: (2026)

A Geometric Framework for Adversarial Vulnerability in Machine Learning
di: Bell, Brian
Pubblicazione: (2024)

Logifold: A Geometrical Foundation of Ensemble Machine Learning
di: Jung, Inkee, et al.
Pubblicazione: (2024)

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach
di: Friedl, Katharina, et al.
Pubblicazione: (2025)

TabArena: A Living Benchmark for Machine Learning on Tabular Data
di: Erickson, Nick, et al.
Pubblicazione: (2025)

Machine Learning-Based Nonlinear Nudging for Chaotic Dynamical Systems
di: Oh, Jaemin, et al.
Pubblicazione: (2025)

Improved Therapeutic Antibody Reformatting through Multimodal Machine Learning
di: Xin, Jiayi, et al.
Pubblicazione: (2025)

Fiber Bundle Networks: A Geometric Machine Learning Paradigm
di: Liu, Dong
Pubblicazione: (2025)

The Maximum von Neumann Entropy Principle: Theory and Applications in Machine Learning
di: Wu, Youqi, et al.
Pubblicazione: (2026)

Evaluating Supervised Machine Learning Models: Principles, Pitfalls, and Metric Selection
di: Liu, Xuanyan, et al.
Pubblicazione: (2026)

Towards a population-informed approach to the definition of data-driven models for structural dynamics
di: Tsialiamanis, G., et al.
Pubblicazione: (2023)

Self-Ablating Transformers: More Interpretability, Less Sparsity
di: Ferrao, Jeremias, et al.
Pubblicazione: (2025)

Multiple-input, multiple-output modal testing of a Hawk T1A aircraft: A new full-scale dataset for structural health monitoring
di: Wilson, James, et al.
Pubblicazione: (2024)

Tropical Geometric Tools for Machine Learning: the TML package
di: Barnhill, David, et al.
Pubblicazione: (2023)

A new Uncertainty Principle in Machine Learning
di: Dolotin, V., et al.
Pubblicazione: (2026)

Geometrically Inspired Kernel Machines for Collaborative Learning Beyond Gradient Descent
di: Kumar, Mohit, et al.
Pubblicazione: (2024)

Towards Principled Learning for Re-ranking in Recommender Systems
di: Li, Qunwei, et al.
Pubblicazione: (2025)

Machine Learning on Dynamic Functional Connectivity: Promise, Pitfalls, and Interpretations
di: Ding, Jiaqi, et al.
Pubblicazione: (2024)

On Procrustes Contamination in Machine Learning Applications of Geometric Morphometrics
di: Courtenay, Lloyd Austin
Pubblicazione: (2026)

Algorithmic Data Minimization for Machine Learning over Internet-of-Things Data Streams
di: Shaowang, Ted, et al.
Pubblicazione: (2025)