Image de couverture de livre

Noether's razor: Learning Conserved Quantities

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	van der Ouderaa, Tycho F. A., van der Wilk, Mark, de Haan, Pim
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Variational Inference Failures Under Model Symmetries: Permutation Invariant Posteriors for Bayesian Neural Networks
par: Gelberg, Yoav, et autres
Publié: (2024)

Leech Lattice Vector Quantization for Efficient LLM Compression
par: van der Ouderaa, Tycho F. A., et autres
Publié: (2026)

Pyramid Vector Quantization for LLMs
par: van der Ouderaa, Tycho F. A., et autres
Publié: (2024)

The LLM Surgeon
par: van der Ouderaa, Tycho F. A., et autres
Publié: (2023)

Learning in Deep Factor Graphs with Gaussian Belief Propagation
par: Nabarro, Seth, et autres
Publié: (2023)

SynDaCaTE: A Synthetic Dataset For Evaluating Part-Whole Hierarchical Inference
par: Levi, Jake, et autres
Publié: (2025)

Symmetry Guarantees Statistic Recovery in Variational Inference
par: Marks, Daniel, et autres
Publié: (2026)

Bivariate Causal Discovery using Bayesian Model Selection
par: Dhir, Anish, et autres
Publié: (2023)

A Meta-Learning Approach to Bayesian Causal Discovery
par: Dhir, Anish, et autres
Publié: (2024)

Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
par: Pescador-Barrios, Guiomar, et autres
Publié: (2024)

A Distributed Gaussian Process Model for Multi-Robot Mapping
par: Nabarro, Seth, et autres
Publié: (2026)

Inverse-Free Sparse Variational Gaussian Processes
par: Cortinovis, Stefano, et autres
Publié: (2026)

Continuous Bayesian Model Selection for Multivariate Causal Discovery
par: Dhir, Anish, et autres
Publié: (2024)

BLIPs: Bayesian Learned Interatomic Potentials
par: Coscia, Dario, et autres
Publié: (2025)

Recommendations for Baselines and Benchmarking Approximate Gaussian Processes
par: Ober, Sebastian W., et autres
Publié: (2024)

Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic Assays
par: Sedgwick, Ruby, et autres
Publié: (2024)

Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
par: Dhir, Anish, et autres
Publié: (2025)

Rethinking Aleatoric and Epistemic Uncertainty
par: Smith, Freddie Bickford, et autres
Publié: (2024)

The Neural Tangent Kernel for Classification
par: Plenk, Jonathan, et autres
Publié: (2026)

Model Selection with Model Zoo via Graph Learning
par: Li, Ziyu, et autres
Publié: (2024)

PSyDUCK: Training-Free Steganography for Latent Diffusion
par: Mahfuz, Aqib, et autres
Publié: (2025)

Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers
par: de Haan, Pim, et autres
Publié: (2023)

Adaptive-TMLE for the Average Treatment Effect based on Randomized Controlled Trial Augmented with Real-World Data
par: van der Laan, Mark, et autres
Publié: (2024)

System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization
par: Qing, Jixiang, et autres
Publié: (2024)

Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
par: Terenin, Alexander, et autres
Publié: (2022)

Statistical learning theory and Occam's razor: The core argument
par: Sterkenburg, Tom F.
Publié: (2023)

Bellman Calibration for $V$-Learning in Offline Reinforcement Learning
par: van der Laan, Lars, et autres
Publié: (2025)

Does equivariance matter at scale?
par: Brehmer, Johann, et autres
Publié: (2024)

In-context learning and Occam's razor
par: Elmoznino, Eric, et autres
Publié: (2024)

Adaptive debiased machine learning using data-driven model selection techniques
par: van der Laan, Lars, et autres
Publié: (2023)

Deep Hierarchical Learning with Nested Subspace Networks for Large Language Models
par: Rauba, Paulius, et autres
Publié: (2025)

Prediction Is Not Physics: Learning and Evaluating Conserved Quantities in Neural Simulators
par: Bukowski, Andrew, et autres
Publié: (2026)

No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs
par: Kacprzyk, Krzysztof, et autres
Publié: (2025)

Deconfounding Imitation Learning with Variational Inference
par: Vuorio, Risto, et autres
Publié: (2022)

Highly Adaptive Ridge
par: Schuler, Alejandro, et autres
Publié: (2024)

Why Tabular Foundation Models Should Be a Research Priority
par: van Breugel, Boris, et autres
Publié: (2024)

Computational Inertia as a Conserved Quantity in Frictionless and Damped Learning Dynamics
par: Karagoz, Atahan
Publié: (2025)

Entangling Machine Learning with Quantum Tensor Networks
par: van der Poel, Constantijn, et autres
Publié: (2024)

Use What You Know: Causal Foundation Models with Partial Graphs
par: Reuter, Arik, et autres
Publié: (2026)

Not All Explanations for Deep Learning Phenomena Are Equally Valuable
par: Jeffares, Alan, et autres
Publié: (2025)