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 [1].
Monomial Matrix Group Equivariant Neural Functional Networks
Authors: Hoang Tran, Thieu Vo, Tho Huu, An Nguyen The, Tan Nguyen
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide empirical evidences to demonstrate the advantages of our model over existing baselines, achieving competitive performance and efficiency. [...] We evaluate Monomial-NFNs on three tasks: predicting CNN generalization from weights using Small CNN Zoo [64], weight space style editing, and classifying INRs using INRs data [71]. Experimental results show that our model achieves competitive performance and efficiency compared to existing baselines. |
| Researcher Affiliation | Collaboration | Viet-Hoang Tran Department of Mathematics National University of Singapore EMAIL Thieu N. Vo Department of Mathematics National University of Singapore EMAIL Tho Tran-Huu Department of Mathematics National University of Singapore EMAIL An T. Nguyen FPT Software AI Center EMAIL Tan M. Nguyen Department of Mathematics National University of Singapore EMAIL |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | The code is publicly available at https://github.com/Mathematical AI-NUS/Monomial NFN. |
| Open Datasets | Yes | We employ the Small CNN Zoo [64]... We utilize the dataset from [71], which comprises pretrained INR networks [58] that encode images from the CIFAR-10 [36], MNIST [39], and Fashion MNIST [69] datasets. |
| Dataset Splits | Yes | The original Re LU subset of the CNN Zoo dataset includes 6050 instances for training and 1513 instances for testing. For the Tanh dataset, it includes 5949 training and 1488 testing instances. [...] We use the Binary Cross Entropy (BCE) loss function and train the model for 50 epochs, with early stopping based on τ on the validation set |
| Hardware Specification | Yes | which takes 35 minutes to train on an A100 GPU. |
| Software Dependencies | No | The paper mentions using 'Adam' as an optimizer but does not specify version numbers for programming languages, machine learning frameworks (e.g., PyTorch, TensorFlow), or other key software dependencies. |
| Experiment Setup | Yes | Hyperparameter settings and the number of parameters can be found in Appendix D. [...] The hyperparameters for our model are presented in Table 18. |