Positive Concave Deep Equilibrium Models
Authors: Mateusz Gabor, Tomasz Piotrowski, Renato L. G. Cavalcante
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate the competitiveness of our pc DEQ models against other implicit models. The experiments were carried out on three commonly known computer vision datasets: MNIST, SVHN, and CIFAR-10. |
| Researcher Affiliation | Academia | 1Faculty of Electronics, Photonics, and Microsystems,Wrocław University of Science and Technology, Wrocław, Poland 2Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Toru n, Poland 3Fraunhofer Heinrich Hertz Institute, Berlin, Germany. |
| Pseudocode | No | The paper provides architectural diagrams but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The Pytorch source code of pc DEQs along with examples of use is available at the following link: https://github.com/ mateuszgabor/pcdeq |
| Open Datasets | Yes | The experiments were carried out on three commonly known computer vision datasets: MNIST, SVHN, and CIFAR-10. MNIST dataset consists 70,000 grayscale handwritten digit images. SVHN dataset consists of 99,289 RGB digit images from house numbers. CIFAR-10 consists of 60,000 RGB images of 10 classes. |
| Dataset Splits | No | The paper provides total training and testing examples in Table 4, but does not explicitly specify the size or method for a validation split. |
| Hardware Specification | Yes | The experiments were performed using the Google Colab platform with a NVIDIA Tesla T4 16GB GPU. |
| Software Dependencies | No | The paper mentions using PyTorch and the AdamW optimizer but does not specify their version numbers or other software dependencies with versions. |
| Experiment Setup | Yes | All other hyperparameters for each dataset and architecture are shown in Tables 5, 6 and 7. |