Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Interpreting Equivariant Representations
Authors: Andreas Abildtrup Hansen, Anna Calissano, Aasa Feragen
ICML 2024 | Venue PDF | 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 CΛote 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). |