Declarative nets that are equilibrium models
Authors: Russell Tsuchida, Suk Yee Yong, Mohammad Ali Armin, Lars Petersson, Cheng Soon Ong
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically show that this initialisation scheme improves training stability and performance over random initialisation. |
| Researcher Affiliation | Academia | Data61, CSIRO Canberra, Australia Space & Astronomy, CSIRO Epping, Australia Machine Learning & Artiļ¬cial Intelligence Future Science Platform |
| Pseudocode | No | The paper describes methods mathematically and textually but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We have included code and the instructions required to reproduce our results. The code is publically available at https://github.com/Russell Tsuchida/deq-glm. |
| Open Datasets | Yes | Image denoising. We consider a convolutional architecture... applied to an image denoising task using the CIFAR10 dataset. |
| Dataset Splits | No | The paper specifies training and test set sizes (e.g., 'training and test set of size N = 20, 000 and 2, 000') but does not explicitly mention a separate validation set split or its proportions. |
| Hardware Specification | Yes | Empirically measured cost (in seconds) for initialisation schemes measured on a DELL Laptop (16GB RAM, Intel Core TM i7-8665U CPU), averaged over 100 runs. |
| Software Dependencies | No | The paper mentions 'Pytorch default' for initialization and 'Adam' for training but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We train for 400 epochs using Adam with default hyperparameters. We repeat this for 100 trials using seeds 0 to 99, see Figure 5(a) and (b). |