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SE(3) Equivariant Augmented Coupling Flows

NeurIPS 2023 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Expressive invertible architectures · 3 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use SE(3)-equivariant EGNN with projection instead of non-rotation-equivariant transformer with augmentation (ablation)E-ACF · QM9 positionalNLL: -76.76 -> -165.71Helped
same replacement (ablation)E-ACF · DW4NLL: 10.07 -> 8.61Helped
same replacement (ablation)E-ACF · LJ13NLL: 33.32 -> 30.33Helped
same replacement, alanine dipeptide (ablation)E-ACF · Alanine dipeptideNLL: -184.57 -> -188.57Helped
same replacement, energy-based FAB training (ablation)E-ACF · DW4 FABReverse ESS (%): 35.94 -> 80.44Helped
same replacement, energy-based FAB training (ablation)E-ACF · LJ13 FABReverse ESS (%): 5.38 -> 62.09Helped
Spherical-proj instead of Vector-proj (ablation)E-ACF · QM9 positionalNLL: -152.23 -> -165.71Helped
Spherical-proj instead of Cartesian-proj (ablation)E-ACF · QM9 positionalNLL: -138.62 -> -165.71Helped
Vector-proj instead of Spherical-proj (ablation)E-ACF · LJ13NLL: 30.19 -> 30.33Hurt
Cartesian-proj instead of Spherical-proj (ablation)E-ACF · Alanine dipeptideReverse ESS (%): 48.4 -> 52.5Mixed
E-ACF versus E-CNF FME-ACF · QM9 positionalNLL: -138.23 -> -165.71Helped
E-ACF versus E-CNF-DiffE-ACF · DW4NLL: 8.01 -> 8.61Hurt

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