Symmetry-induced Disentanglement on Graphs
Authors: Giangiacomo Mercatali, Andre Freitas, Vikas Garg
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real datasets suggest that these models can learn effective disengaged representations, and improve performance on downstream tasks such as few-shot classification and molecular generation. We conducted extensive experiments that we decribe now. Section 5.1 evaluates the disentanglement capabilities of the models, Section 5.2 provides a compression and few-shots classification experiment, and Section 5.3 assesses the generation capabilities on molecular datasets. |
| Researcher Affiliation | Collaboration | Giangiacomo Mercatali University of Manchester André Freitas Idiap Research Institute University of Manchester Vikas Garg Yai Yai Ltd and Aalto University |
| Pseudocode | No | The paper describes its architecture and probabilistic formulation in detail, including equations and a diagram (Figure 1), but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] Provided at https://mercatali.gitlab.io/sid/ |
| Open Datasets | Yes | For metrics, we follow the evaluation protocols from Locatello et al. [36]... We compute 5 metrics, including β-VAE [19]... The task consists in training first without labels to learn the representation, and then using the compressed model as input for training a GCN classifier a labelled subset of the dataset, which involves 100 samples for training and 100 for testing. ...including FRANKENSTEIN [43] (2 classes), COLORS-3 [30] (11 classes), Mutagenicity [51] (2 classes), NCI1 [53] (2 c). ...results on ZINC [23], QM9 [49], and MOSES [47]. |
| Dataset Splits | No | The paper mentions 100 samples for training and 100 for testing in the few-shot classification task, but it does not specify a separate validation set or its size/proportion for model training or hyperparameter tuning. |
| Hardware Specification | No | The paper states, 'We specify the experimental setups in Section 5,' but Section 5 does not contain any specific hardware details such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions 'Torch Drug2' and 'DIG' as approaches implemented via, and provides a URL for Torch Drug2, but it does not specify version numbers for these or any other software components. |
| Experiment Setup | Yes | We set our models to have a group size of 81, a hessian penalty of 40, and a commutative penalty of 5. All models are set to have a pooling rate of 0.8, and a depth of 3 layers. Our models are set with a group size of 81, hessian penalty of 20 and commutative penalty of 5. We train our models for 10 epochs, with a batch size of 32 and the learning rate of 0.001, and compute the metrics over 10K generated molecules. |