Interpretable Meta-Learning of Physical Systems
Fuente:
arXiv
Saved in:
| Main Authors: | Blanke, Matthieu, Lelarge, Marc |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Neural Incremental Data Assimilation
by: Blanke, Matthieu, et al.
Published: (2024)
by: Blanke, Matthieu, et al.
Published: (2024)
Chaining 2-FWL GNNs for Combinatorial Graph Alignment
by: Lelarge, Marc
Published: (2025)
by: Lelarge, Marc
Published: (2025)
Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodings
by: Lagesse, Adrien, et al.
Published: (2025)
by: Lagesse, Adrien, et al.
Published: (2025)
Strictly Constrained Generative Modeling via Split Augmented Langevin Sampling
by: Blanke, Matthieu, et al.
Published: (2025)
by: Blanke, Matthieu, et al.
Published: (2025)
Correlation detection in trees for planted graph alignment
by: Ganassali, Luca, et al.
Published: (2021)
by: Ganassali, Luca, et al.
Published: (2021)
Random Sparse Lifts: Construction, Analysis and Convergence of finite sparse networks
by: Robin, David A. R., et al.
Published: (2025)
by: Robin, David A. R., et al.
Published: (2025)
PnP-DA: Towards Principled Plug-and-Play Integration of Variational Data Assimilation and Generative Models
by: Qu, Yongquan, et al.
Published: (2025)
by: Qu, Yongquan, et al.
Published: (2025)
Putnam 2025 Problems in Rocq using Opus 4.6 and Rocq-MCP
by: Baudart, Guillaume, et al.
Published: (2026)
by: Baudart, Guillaume, et al.
Published: (2026)
MiniF2F in Rocq: Automatic Translation Between Proof Assistants -- A Case Study
by: Viennot, Jules, et al.
Published: (2025)
by: Viennot, Jules, et al.
Published: (2025)
High-Order Deep Meta-Learning with Category-Theoretic Interpretation
by: Mguni, David H.
Published: (2025)
by: Mguni, David H.
Published: (2025)
MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration
by: Bhattacharya, Rishabh, et al.
Published: (2025)
by: Bhattacharya, Rishabh, et al.
Published: (2025)
KKL Observer Synthesis for Nonlinear Systems via Physics-Informed Learning
by: Niazi, M. Umar B., et al.
Published: (2025)
by: Niazi, M. Umar B., et al.
Published: (2025)
Meta Additive Model: Interpretable Sparse Learning With Auto Weighting
by: Zhang, Xuelin, et al.
Published: (2026)
by: Zhang, Xuelin, et al.
Published: (2026)
Interpretability and Generalization Bounds for Learning Spatial Physics
by: Queiruga, Alejandro Francisco, et al.
Published: (2025)
by: Queiruga, Alejandro Francisco, et al.
Published: (2025)
Meta-Learning for Physically-Constrained Neural System Identification
by: Chakrabarty, Ankush, et al.
Published: (2025)
by: Chakrabarty, Ankush, et al.
Published: (2025)
From model-based learning to model-free behaviour with Meta-Interpretive Learning
by: Patsantzis, Stassa
Published: (2025)
by: Patsantzis, Stassa
Published: (2025)
Physics-Aware Machine Learning for Seismic and Volcanic Signal Interpretation
by: Thorossian, William
Published: (2026)
by: Thorossian, William
Published: (2026)
Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems
by: Wu, Bibo, et al.
Published: (2025)
by: Wu, Bibo, et al.
Published: (2025)
SOLIS: Physics-Informed Learning of Interpretable Neural Surrogates for Nonlinear Systems
by: Mansur, Murat Furkan, et al.
Published: (2026)
by: Mansur, Murat Furkan, et al.
Published: (2026)
Mechanistic Interpretability for Learning Assurance of a Vision-Based Landing System
by: Valentin, Romeo, et al.
Published: (2026)
by: Valentin, Romeo, et al.
Published: (2026)
Trivial Graph Features and Classical Learning are Enough to Detect Random Anomalies
by: Latapy, Matthieu, et al.
Published: (2026)
by: Latapy, Matthieu, et al.
Published: (2026)
Physics-Inspired Interpretability Of Machine Learning Models
by: Niroomand, Maximilian P, et al.
Published: (2023)
by: Niroomand, Maximilian P, et al.
Published: (2023)
Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction
by: Baier, Alexandra, et al.
Published: (2021)
by: Baier, Alexandra, et al.
Published: (2021)
Physically Interpretable World Models via Weakly Supervised Representation Learning
by: Mao, Zhenjiang, et al.
Published: (2024)
by: Mao, Zhenjiang, et al.
Published: (2024)
Measurement for Opaque Systems: Multi-source Triangulation with Interpretable Machine Learning
by: Foster, Margaret
Published: (2026)
by: Foster, Margaret
Published: (2026)
GlassMol: Interpretable Molecular Property Prediction with Concept Bottleneck Models
by: Rivera, Oscar, et al.
Published: (2026)
by: Rivera, Oscar, et al.
Published: (2026)
MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning
by: Zhang, Baoquan, et al.
Published: (2023)
by: Zhang, Baoquan, et al.
Published: (2023)
Explanations Go Linear: Post-hoc Explainability for Tabular Data with Interpretable Meta-Encoding
by: Piaggesi, Simone, et al.
Published: (2025)
by: Piaggesi, Simone, et al.
Published: (2025)
Interpretable ML Under the Microscope: Performance, Meta-Features, and the Regression-Classification Predictability Gap
by: Billa, Mattia, et al.
Published: (2026)
by: Billa, Mattia, et al.
Published: (2026)
Policy Optimization via Adv2: Adversarial Learning on Advantage Functions
by: Jonckheere, Matthieu, et al.
Published: (2023)
by: Jonckheere, Matthieu, et al.
Published: (2023)
Meta Learning not to Learn: Robustly Informing Meta-Learning under Nuisance-Varying Families
by: McConnell, Louis
Published: (2025)
by: McConnell, Louis
Published: (2025)
Physically Interpretable Representation Learning with Gaussian Mixture Variational AutoEncoder (GM-VAE)
by: Fan, Tiffany, et al.
Published: (2025)
by: Fan, Tiffany, et al.
Published: (2025)
Online Matching via Reinforcement Learning: An Expert Policy Orchestration Strategy
by: Mignacco, Chiara, et al.
Published: (2025)
by: Mignacco, Chiara, et al.
Published: (2025)
Learning Interpretable Low-dimensional Representation via Physical Symmetry
by: Liu, Xuanjie, et al.
Published: (2023)
by: Liu, Xuanjie, et al.
Published: (2023)
Neural Context Flows for Meta-Learning of Dynamical Systems
by: Nzoyem, Roussel Desmond, et al.
Published: (2024)
by: Nzoyem, Roussel Desmond, et al.
Published: (2024)
Neuromodulated Meta-Learning
by: Wang, Jingyao, et al.
Published: (2024)
by: Wang, Jingyao, et al.
Published: (2024)
MISS: Multiclass Interpretable Scoring Systems
by: Grzeszczyk, Michal K., et al.
Published: (2024)
by: Grzeszczyk, Michal K., et al.
Published: (2024)
System Prompt Optimization with Meta-Learning
by: Choi, Yumin, et al.
Published: (2025)
by: Choi, Yumin, et al.
Published: (2025)
Closing the Gap between TD Learning and Supervised Learning -- A Generalisation Point of View
by: Ghugare, Raj, et al.
Published: (2024)
by: Ghugare, Raj, et al.
Published: (2024)
Interpretable Machine Learning in Physics: A Review
by: Wetzel, Sebastian Johann, et al.
Published: (2025)
by: Wetzel, Sebastian Johann, et al.
Published: (2025)
Similar Items
-
Neural Incremental Data Assimilation
by: Blanke, Matthieu, et al.
Published: (2024) -
Chaining 2-FWL GNNs for Combinatorial Graph Alignment
by: Lelarge, Marc
Published: (2025) -
Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodings
by: Lagesse, Adrien, et al.
Published: (2025) -
Strictly Constrained Generative Modeling via Split Augmented Langevin Sampling
by: Blanke, Matthieu, et al.
Published: (2025) -
Correlation detection in trees for planted graph alignment
by: Ganassali, Luca, et al.
Published: (2021)