Weisfeiler Leman for Euclidean Equivariant Machine Learning
Authors: Snir Hordan, Tal Amir, Nadav Dym
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate our claims via a separation experiment on combinatorial graphs that are 2-WL distinguishable, yet are 1-WL indistinguishable, via the EXP (Abboud et al., 2020) dataset. |
| Researcher Affiliation | Academia | 1Faculty of Mathematics, Technion Israel Institute of Technology, Haifa, Israel 2Faculty of Computer Science, Technion Israel Institute of Technology, Haifa, Israel. |
| Pseudocode | Yes | We define the Convolution Layer of We LNet , i.e. We LConv, which is based on EGNN, as c(i, j) = PPGNan(θ; , T)(X, V )i,j (10) mij = ϕe(hi, hj, eij, c(i, j)) (11) mi = j mi,j (12) xout i = xi + ϕn(mi)vi + X j ϕx(mi,j)(xj xi) + X j =i ϕv(mi,j)vj (13) vout i = ˆ ϕn(mi)vi + X j ˆϕx(mi,j)(xj xi) + X j =i ˆϕv(mi,j)vj (14) hout i = ϕh(hi, mi) (15) |
| Open Source Code | Yes | Code is available at https://www.github.com/ Intelli Finder/welnet |
| Open Datasets | Yes | We empirically evaluate our claims via a separation experiment on combinatorial graphs that are 2-WL distinguishable, yet are 1-WL indistinguishable, via the EXP (Abboud et al., 2020) dataset. |
| Dataset Splits | Yes | Following (Victor Garcia Satorras, 2021), we sampled 3,000 trajectories for training, 2,000 for validation and 2.000 for testing. |
| Hardware Specification | Yes | We ran our experiment on an NVIDIA A40 GPU with CUDA toolkit version 12.1. |
| Software Dependencies | Yes | We ran our experiment on an NVIDIA A40 GPU with CUDA toolkit version 12.1. |
| Experiment Setup | Yes | Table 4. Configuration of the We LNet Architecture. HYPERPARAMETER VALUE ACTIVATION SCALED SOFTPLUS EDGE FEATURES DIM 128 WL FEATURES DIM 32 LEARNING RATE 1E-3 OPTIMIZER ADAM SCHEDULER STEPLR NUMBER OF CONVOLUTIONS 4 2-WL ITERATIONS (T) 2 |