Advection Augmented Convolutional Neural Networks
Authors: Niloufar Zakariaei, Siddharth Rout, Eldad Haber, Moshe Eliasof
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our network on a number of spatio-temporal datasets that show their merit. |
| Researcher Affiliation | Academia | Niloufar Zakariaei University of British Columbia Vancouver, Canada nilouzk@student.ubc.ca Siddharth Rout University of British Columbia Vancouver, Canada siddharth.rout@ubc.ca Eldad Haber University of British Columbia Vancouver, Canada ehaber@eoas.ubc.ca Moshe Eliasof University of Cambridge Cambridge, United Kingdom me532@cam.ac.uk |
| Pseudocode | Yes | Algorithm 1 The ADR network |
| Open Source Code | Yes | Our code is available at https://github.com/Siddharth-Rout/deep ADRnet. |
| Open Datasets | Yes | We use two such datasets, Cloud Cast [70], and the Shallow Water Equation in PDEbench [51]. Moving MNIST. The Moving MNIST dataset is a synthetic video dataset designed to test sequence prediction models. KITTI. The KITTI is a widely recognized dataset extensively used in mobile robotics and autonomous driving, and it also serves as a benchmark for computer vision algorithms. Taxi BJ [70] and KTH [45]. |
| Dataset Splits | No | Table 1: Datasets statistics. Training and testing splits, image sequences, and resolutions. Table 1 and Table 7 list Ntrain and Ntest for each dataset, but do not provide explicit details about a separate validation split, its size, or how it was derived. |
| Hardware Specification | Yes | We run our codes using a single NVIDIA RTX-A6000 GPU with 48GB of memory. All our experiments are conducted using an NVIDIA RTX-A6000 GPU with 48GB of memory. |
| Software Dependencies | No | The advection term is implemented by using the sample Grid command in Py Torch [41]. While PyTorch is mentioned, no specific version number is provided for it or any other software dependency to ensure reproducibility. |
| Experiment Setup | Yes | Table 16: Neural Network Hyperparameters [for] ADRNet Training on PDEBench-SWE, detailing Learning Rate, Batch Size, Number of Epochs, Optimizer, Number of Layers, Hidden Channels, and Activation Function. Similar details are provided in Table 17 for other datasets. |