ICML 2024 "geometric deep learning" Papers
8 papers found
An Intrinsic Vector Heat Network
Alexander Gao, Maurice Chu, Mubbasir Kapadia et al.
ICML 2024posterarXiv:2406.09648
Exploring the Enigma of Neural Dynamics Through A Scattering-Transform Mixer Landscape for Riemannian Manifold
Tingting Dan, Ziquan Wei, Won Hwa Kim et al.
ICML 2024posterarXiv:2405.16357
Generative Modeling on Manifolds Through Mixture of Riemannian Diffusion Processes
Jaehyeong Jo, Sung Ju Hwang
ICML 2024posterarXiv:2310.07216
GeoMFormer: A General Architecture for Geometric Molecular Representation Learning
Tianlang Chen, Shengjie Luo, Di He et al.
ICML 2024posterarXiv:2406.16853
Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics
Siqi Miao, Zhiyuan Lu, Mia Liu et al.
ICML 2024posterarXiv:2402.12535
O$n$ Learning Deep O($n$)-Equivariant Hyperspheres
Pavlo Melnyk, Michael Felsberg, Mårten Wadenbäck et al.
ICML 2024posterarXiv:2305.15613
Position: Categorical Deep Learning is an Algebraic Theory of All Architectures
Bruno Gavranović, Paul Lessard, Andrew Dudzik et al.
ICML 2024posterarXiv:2402.15332
Position: Topological Deep Learning is the New Frontier for Relational Learning
Theodore Papamarkou, Tolga Birdal, Michael Bronstein et al.
ICML 2024posterarXiv:2402.08871