REVIEW 12 cited by
Frame Averaging for Invariant and Equivariant Network Design
Not yet reviewed by Pith; the record is open.
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
Signed reviews
abstract
Many machine learning tasks involve learning functions that are known to be invariant or equivariant to certain symmetries of the input data. However, it is often challenging to design neural network architectures that respect these symmetries while being expressive and computationally efficient. For example, Euclidean motion invariant/equivariant graph or point cloud neural networks. We introduce Frame Averaging (FA), a general purpose and systematic framework for adapting known (backbone) architectures to become invariant or equivariant to new symmetry types. Our framework builds on the well known group averaging operator that guarantees invariance or equivariance but is intractable. In contrast, we observe that for many important classes of symmetries, this operator can be replaced with an averaging operator over a small subset of the group elements, called a frame. We show that averaging over a frame guarantees exact invariance or equivariance while often being much simpler to compute than averaging over the entire group. Furthermore, we prove that FA-based models have maximal expressive power in a broad setting and in general preserve the expressive power of their backbone architectures. Using frame averaging, we propose a new class of universal Graph Neural Networks (GNNs), universal Euclidean motion invariant point cloud networks, and Euclidean motion invariant Message Passing (MP) GNNs. We demonstrate the practical effectiveness of FA on several applications including point cloud normal estimation, beyond $2$-WL graph separation, and $n$-body dynamics prediction, achieving state-of-the-art results in all of these benchmarks.
Forward citations
Cited by 12 Pith papers
-
Inverting Data Transformations via Diffusion Sampling
A Lie-group diffusion sampler that inverts unknown data transformations at test time, using only an energy function, and improves pretrained models on affine/homography images and PDE solving.
-
Gene Ontology-Guided Hierarchical Spatial Gene Expression Prediction from Histopathology Images
MSGR's Gene Ontology-guided hierarchical decoder improves spatial gene expression prediction from histology images, with the biological structure adding a +0.027 gain over an equivalent random hierarchy.
-
Platonic Transformers: A Solid Choice For Equivariance
Platonic Transformers achieve exact equivariance to translations plus discrete Platonic-solid rotations by lifting features into multiple reference frames and sharing one RoPE attention across them, with a linear-time...
-
All-atom inverse protein folding through discrete flow matching
ADFLIP applies discrete flow matching with progressive side-chain packing to achieve state-of-the-art sequence recovery on all-atom inverse folding benchmarks, including multi-state NMR ensembles.
-
BindEnergyCraft: Casting Protein Structure Predictors as Energy-Based Models for Binder Design
pTMEnergy converts AlphaFold pAE confidence logits into an energy-like score that improves computational binder design success and virtual screening over ipTM-based and generative baselines.
-
Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matching
STFlow uses whole-slide flow matching with local spatial attention to jointly predict gene expression across all spots in a histology image, outperforming prior spot-wise and slide-wise baselines on two benchmarks.
-
Multi-Alignment Contrastive Learning for Enzyme--Reaction Retrieval
FGW-CLIP, a contrastive method that aligns enzymes and reactions while also aligning within-domain EC structure with a Gromov-Wasserstein regularizer, reports state-of-the-art retrieval on EnzymeMap and ReactZyme.
-
Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning
IPBind, a frame-averaged graph neural network, predicts binding affinity as a sum of atomic energy differences and claims state-of-the-art accuracy on the low-sequence-identity LBA30 and LBA60 benchmarks.
-
TAGTorch: A PyTorch Library for Geometry, Topology, and Symmetry-Aware Machine Learning
TAGTorch packages geometry, topology, and symmetry-aware deep learning tools into one PyTorch library, with a design built around unified data transforms and group representations.
-
EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks
A plug-and-play combination of an equivariant GNN encoder and a hypergraph network with conjugated-bond hyperedges improves property prediction on large molecules, with inconsistent gains on small molecules.
-
FA-KPConv: Introducing Euclidean Symmetries to KPConv via Frame Averaging
Frame Averaging applied to KPConv yields FA-KPConv, a parameter-free wrapper that makes KPConv networks exactly Euclidean invariant or equivariant, improving robustness on rotated and low-data benchmarks.
-
Graph Neural Networks in Modern AI-aided Drug Discovery
A comprehensive model-centric review of graph neural network methods and applications in AI-aided drug discovery, from molecular representation to synthesis planning.
Discussion (0). Continue with ORCID to comment.