Pith. sign in

REVIEW 10 cited by

SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2006.10503 v3 pith:UUM6WBIY submitted 2020-06-18 cs.LG stat.ML

Fabian B. Fuchs , Daniel E. Worrall , Volker Fischer , Max Welling This is my paper
classification cs.LGstat.ML
keywords modelattentionequivarianceequivariantcloudsgraphsinputperformance
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We introduce the SE(3)-Transformer, a variant of the self-attention module for 3D point clouds and graphs, which is equivariant under continuous 3D roto-translations. Equivariance is important to ensure stable and predictable performance in the presence of nuisance transformations of the data input. A positive corollary of equivariance is increased weight-tying within the model. The SE(3)-Transformer leverages the benefits of self-attention to operate on large point clouds and graphs with varying number of points, while guaranteeing SE(3)-equivariance for robustness. We evaluate our model on a toy N-body particle simulation dataset, showcasing the robustness of the predictions under rotations of the input. We further achieve competitive performance on two real-world datasets, ScanObjectNN and QM9. In all cases, our model outperforms a strong, non-equivariant attention baseline and an equivariant model without attention.

Discussion (0). Sign in to comment.

Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. A Unified Framework for Vision Transformers Equivariant to Discrete Subgroups of $\mathrm{O}(2)$

    cs.CV 2026-06 unverdicted novelty 7.0 of 10

    A unified family of vision transformers equivariant to arbitrary discrete subgroups of O(2), with embedding and expressivity theorems, a D6 construction using hexagonal patches, and experiments on aerial images in low...

  2. A Unified Framework for Vision Transformers Equivariant to Discrete Subgroups of $\mathrm{O}(2)$

    cs.CV 2026-06 unverdicted novelty 7.0 of 10

    Constructs G-equivariant ViTs for arbitrary discrete G ≤ O(2), proves H ≤ G implies G-models embed into H-models and single-head equivariant attention realizes all ordinary G-equivariant maps, introduces D6 hexagonal ...

  3. Fast contracted Clebsch--Gordan tensor products for equivariant graph neural networks

    physics.comp-ph 2026-05 unverdicted novelty 7.0 of 10

    An O(L^3) algorithm computes contracted Clebsch-Gordan tensor products for equivariant ML potentials using a structured angular grid and spherical Poisson bracket to handle parity-odd terms at fixed CP rank.

  4. Chem-GMNet: A Sphere-Native Geometric Transformer for Molecular Property Prediction

    cs.LG 2026-05 unverdicted novelty 7.0 of 10

    Chem-GMNet uses sphere-native embeddings, DualSKA attention, and SH-FFN layers to match or beat ChemBERTa-2 on MoleculeNet tasks with fewer parameters and sometimes no pretraining.

  5. Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular Dynamics

    cs.LG 2026-04 conditional novelty 6.5 of 10

    Structure-pretrained diffusion plus an equivariant temporal interpolator generates chemically realistic MD trajectories on small molecules, tetrapeptides, and proteins by separating spatial and temporal learning.

  6. Transformer Atomic Cluster Expansion: TRACE

    cond-mat.mtrl-sci 2026-07 conditional novelty 6.0 of 10

    A no-message-passing, attention-based local potential reproduces a perovskite phase transition, liquid-water O–O structure, and an organic rearrangement barrier with a single architecture.

  7. Velocityformer: Broken-Symmetry-Matched Equivariant Graph Transformers for Cosmological Velocity Reconstruction

    astro-ph.CO 2026-05 unverdicted novelty 6.0 of 10

    Velocityformer achieves 35% higher velocity correlation than linear theory by matching graph transformer inductive bias to the line-of-sight broken symmetry and conditioning on long-wavelength physics, while training ...

  8. ATOM: A Pretrained Neural Operator for Multitask Molecular Dynamics

    cs.LG 2025-10 unverdicted novelty 6.0 of 10

    ATOM is a quasi-equivariant transformer neural operator pretrained on the TG80 dataset that achieves SOTA single-task MD performance and strong zero-shot generalization to unseen molecules and time horizons.

  9. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

    cs.LG 2021-04 accept novelty 6.0 of 10

    Geometric deep learning provides a unified mathematical framework based on grids, groups, graphs, geodesics, and gauges to explain and extend neural network architectures by incorporating physical regularities.

  10. Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems

    cs.LG 2026-06 unverdicted novelty 5.0 of 10

    GraMO couples graph interactions and temporal state updates in one linear recurrence with input-dependent coefficients to simulate N-body, motion, and robotics systems with lower long-horizon error than prior GNN or S...

Pith tools