- ICML 2024 Papers🔍
- [1902.04615] Gauge Equivariant Convolutional Networks and the ...🔍
- [PDF] Equivariant Graph Neural Operator for Modeling 3D Dynamics🔍
- Learning Local Equivariant Representations for Large|Scale ...🔍
- an Equivariant Neural Network?🔍
- A Gaussian Process and Input Encoding for Operator Learning🔍
Equivariant neural operators for gradient|consistent topology ...
TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision ... Verifying message-passing neural networks via topology- ...
[1902.04615] Gauge Equivariant Convolutional Networks and the ...
Abstract: The principle of equivariance to symmetry transformations enables a theoretically grounded approach to neural network architecture ...
[PDF] Equivariant Graph Neural Operator for Modeling 3D Dynamics
Equivariant Graph Neural Operator is the first operator learning framework that is capable of modeling solution dynamics functions over time ...
Learning Local Equivariant Representations for Large-Scale ...
Try datamol.io - the open source toolkit that simplifies molecular processing and featurization workflows for machine learning scientists ...
Lecture 6: Gauge-equivariant Mesh CNN - Pim de Haan - YouTube
Video recording of the First Italian School on Geometric Deep Learning held in Pescara in July 2022.
an Equivariant Neural Network? - NSF PAR
continuous equivariant maps are well-studied in topology. The novelty of ... Although an equivariant neural network is nonlinear, it uses intertwining operators.
A Gaussian Process and Input Encoding for Operator Learning
A Gaussian Process and Input Encoding for Operator Learning || Topology ... AttnPINNs || Improving Spectral Bias in Neural Operators || SympGNNs ...
INO: Invariant Neural Operators for Learning Complex Physical ...
Resolu- tion independence empowers the learned operator to retain consistent accuracy in prediction regardless of the variation ... ployed in the equivariant ...