QuBD extends algorithmic complexity estimation to quantized DNN weights, revealing that complexity decreases during learning, increases with overfitting, follows grokking patterns, and correlates with generalization.
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Does equivariance matter at scale?arXiv preprint arXiv:2410.23179,
10 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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Feature-space sampling in GCNNs preserves 3D classification accuracy with coarse discretization, enabling precomputation and faster training of equivariant models.
Conformal calibration produces orbit-valid trust horizons for equivariant world models, with zero violations in 50 audits and non-vacuous certificates on 2D/3D substrates.
Derives exact equivariance conditions for augmented BNNs under variational inference and proposes orbit expansion symmetrization that outperforms baselines on equivariance and accuracy.
Muon optimizer improves performance over Adam in equivariant networks on ModelNet40 and produces solutions with larger Hessian curvature, more regular loss surfaces, and higher stable/effective ranks.
Explicit E(3)-equivariance in neural CFD surrogates improves generalization on diverse-geometry hemodynamics benchmarks but degrades in-distribution performance on strongly aligned aerodynamics data, consistently beating data augmentation.
mlip v2 is a new software release that integrates API redesign, e3j backend, eSEN model, improved charge modeling, and expanded simulation capabilities to support larger-scale molecular modeling.
This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.
Review of neural scaling laws and their relation to constraints and inductive biases when applying machine learning to physics problems.
citing papers explorer
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Characterizing Learning in Deep Neural Networks using Tractable Algorithmic Complexity Analysis
QuBD extends algorithmic complexity estimation to quantized DNN weights, revealing that complexity decreases during learning, increases with overfitting, follows grokking patterns, and correlates with generalization.
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Discretizing Group-Convolutional Neural Networks for 3D Geometry in Feature Space
Feature-space sampling in GCNNs preserves 3D classification accuracy with coarse discretization, enabling precomputation and faster training of equivariant models.
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Conformal Orbit-Valid Trust Horizons for Equivariant World Models
Conformal calibration produces orbit-valid trust horizons for equivariant world models, with zero violations in 50 audits and non-vacuous certificates on 2D/3D substrates.
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Equivariance and Augmentation for Bayesian Neural Networks
Derives exact equivariance conditions for augmented BNNs under variational inference and proposes orbit expansion symmetrization that outperforms baselines on equivariance and accuracy.
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How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks
Muon optimizer improves performance over Adam in equivariant networks on ModelNet40 and produces solutions with larger Hessian curvature, more regular loss surfaces, and higher stable/effective ranks.
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Symmetry in the Wild: The Role of Equivariance in Neural Fluid Surrogates
Explicit E(3)-equivariance in neural CFD surrogates improves generalization on diverse-geometry hemodynamics benchmarks but degrades in-distribution performance on strongly aligned aerodynamics data, consistently beating data augmentation.
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Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation
mlip v2 is a new software release that integrates API redesign, e3j backend, eSEN model, improved charge modeling, and expanded simulation capabilities to support larger-scale molecular modeling.
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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.
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Statistical Properties of Training & Generalization
Review of neural scaling laws and their relation to constraints and inductive biases when applying machine learning to physics problems.
- Performance-Carbon Trade-Offs across Architectural Biases in Shear Flow Forecasting