UvA-DARE (Digital Academic Repository) 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data

Subfields: Representation & Self-Supervised Learning, Model Architectures, 3D Vision & Pose

Citations in the corpus

Directional Message Passing for Molecular Graphs (2020, background): “, 2018), volumetric data (Weiler et al., 2018), and general manifolds (Cohen et al.”
Group Equivariant Stand-Alone Self-Attention For Vision (2020, background): “Similar behaviour is seen for steerable G-CNNs, e.g., Weiler et al. (2018b).”
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties (2020, background): “Meanwhile, there has also been development on neural networks which are equivariant to group action [6], some with molecules in mind [7, 8].”
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks (2020, background): “(7)) exchanges information solely between features of the same degree and within one node—much akin to 1x1 convolutions in CNNs.”
Isometric Transformation Invariant and Equivariant Graph Convolutional Networks (2020, background): “, 2018), three-dimensional data (Weiler et al., 2018), and general manifolds (Cohen et al.”
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data (2020, methodology): “…approach has been applied on general data types like spherical images (Esteves et al., 2018; Cohen et al., 2018; Jiang et al., 2019), voxel data (Weiler et al., 2018), and point clouds (Thomas et al., 2018; Anderson et al., 2019), the requirement of working out the representation theory for the…”
Set2Graph: Learning Graphs From Sets (2020, background): “…images with rotational and reﬂectional symmetries (Cohen & Welling, 2016a;b; Dieleman et al., 2016; Worrall et al., 2017), learning functions on spheres (Cohen et al., 2018; Esteves et al., 2017) and learning general 3D data (Weiler et al., 2018; Worrall & Brostow, 2018; Weiler et al., 2018).”
General E(2)-Equivariant Steerable CNNs (2019, methodology): “Sections 2.2 and 2.3 brieﬂy review the theory of Euclidean steerable CNNs as described in [2].”
Cormorant: Covariant Molecular Neural Networks (2019, methodology): “A key difference between Cormorant and other recent covariant networks (Tensor Field Networks [Thomas et al., 2018] and SE (3) -equivariant networks [Weiler et al., 2018]) is the use of Clebsch-Gordan non-linearities.”
Gauge Equivariant Convolutional Networks and the Icosahedral CNN (2019, background): “…Maron et al., 2019), translations and rotations of the plane and 3D space (Oyallon & Mallat, 2015; Cohen & Welling, 2016; 2017; Marcos et al., 2017; Weiler et al., 2018b;a; Worrall et al., 2017; Worrall & Brostow, 2018; Winkels & Cohen, 2018; Winkens et al., 2018; Thomas et al., 2018; Bekkers et…”
Protein sequence design with a learned potential (2022, methodology): “For the amino-acid type prediction task, our conditional model achieves a 57.3% test set accuracy, either outperforming 40 or matching 26,41,42 previously reported machine learning models for the same task.”
GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation (2022, background): “…is ubiquitous in machine learning for atomic systems, e.g. , the vectors of atomic dipoles or forces should rotate accordingly w.r.t. the conformation coordinates (Thomas et al., 2018; Weiler et al., 2018; Fuchs et al., 2020; Miller et al., 2020; Simm et al., 2021; Batzner et al., 2021).”
Learning Gradient Fields for Molecular Conformation Generation (2021, background): “, 3D roto-translation equivariance of molecular conformations or point clouds (Thomas et al., 2018; Weiler et al., 2018; Fuchs et al., 2020; Miller et al., 2020; Simm et al., 2021).”
Protein sequence design with a learned potential (2020, methodology): “Previous machine learning models for this particular task have only been applied to architecture class, secondary structure, and ∆∆G prediction [66, 67, 68].”
End-to-End Learning on 3D Protein Structure for Interface Prediction (2018, background): “Equivariant networks represent another recent and exciting line of work extending these symmetry functions [11], [12], [13].”