Interpreting Equivariant Representations
Authors: Andreas Abildtrup Hansen, Anna Calissano, Aasa Feragen
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the effect of the suggested tools via widely encountered group actions on two widely used model classes: 1) A permutation equivariant variational autoencoder (VAE) representing molecular graphs acted on by node permutations, where we obtain isometric invariant representations of the data, and 2) an equivariant representations of a rotation-invariant image classifier, where we showcase random invariant projections as a general and efficient tool for providing expressive invariant representations. |
| Researcher Affiliation | Academia | 1Department of Visual Computing, Technical University of Denmark, Kgs. Lyngby, Denmark 2INRIA d Universit e Cˆote d Azur, France 3Now at: Department of Mathematics, Imperial College London, London, England. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | Dataset: The QM9 dataset (Ramakrishnan et al., 2014; Ruddigkeit et al., 2012) consists of approx. 130.000 stable, small molecules, using 80%/10%/10% for training/validation/testing. |
| Dataset Splits | Yes | Dataset: The QM9 dataset (Ramakrishnan et al., 2014; Ruddigkeit et al., 2012) consists of approx. 130.000 stable, small molecules, using 80%/10%/10% for training/validation/testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like the 'Python Geometric library (Fey & Lenssen, 2019)', 'pytorch library(Paszke et al., 2017)', and 'ESCNN library provided by (Weiler & Cesa, 2019; Cesa et al., 2022b)' but does not provide specific version numbers for these software components or programming languages. |
| Experiment Setup | Yes | Training details: The model was trained using the negative evidence lower bound (ELBO) as is standard for VAEs. A learning rate of 0.0001 and a batch-size of 32 was chosen. The model was trained for 1000 epochs. The QM9 dataset was obtained through the Python Geometric library (Fey & Lenssen, 2019). [...] Training details: The model was trained using a cross-entropy loss. A learning rate of 0.01 and a batch-size of 128 was chosen. The model was trained for 100 epochs. The MNIST dataset was obtained through the pytorch library(Paszke et al., 2017). |