Latent Time Neural Ordinary Differential Equations
Authors: Srinivas Anumasa, P. K. Srijith6010-6018
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments to evaluate the uncertainty and robustness modelling capabilities of the proposed approaches2, LT-NODE and ALT-NODE using synthetic and real-world data sets. The approaches are compared against standard NODE (Chen et al. 2018) and baselines which were recently proposed to model uncertainty in the NODE models, such as NODE-GP (Anumasa and Srijith 2021a) and SDE-Net (Kong, Sun, and Zhang 2020). |
| Researcher Affiliation | Academia | Srinivas Anumasa, P.K. Srijith Indian Institute of Technology Hyderabad, India cs16resch11004@iith.ac.in,srijith@cse.iith.ac.in |
| Pseudocode | Yes | Algorithm 1: Forward pass in LT-NODE, computing predictive probability for datapoint x. |
| Open Source Code | Yes | 2https://github.com/srinivas-quan/LTNODE |
| Open Datasets | Yes | We demonstrate the superior uncertainty modelling capability of LT-NODE and ALT-NODE under different experimental setups on synthetic and several real-world image classification data sets such as CIFAR10 (Krizhevsky and Hinton 2009), SVHN (Netzer et al. 2011), MNIST (Le Cun et 1998) and Fashion-MNIST (Xiao, Rasul, and Vollgraf 2017). |
| Dataset Splits | No | The paper mentions training data and test data, but does not explicitly state the percentages or counts for train/validation/test splits, nor does it refer to specific predefined splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify particular software dependencies with version numbers, such as specific deep learning frameworks or libraries. |
| Experiment Setup | No | The paper mentions that 'All the models follow the same architecture as standard NODE. Additional networks are required for SDE-Net for diffusion and ALT-NODE for inference, both using 3 convolution layers followed by a fully connected layer.' and that 'adaptive numerical technique such as Dopri5' is used. However, it does not provide specific hyperparameters like learning rate, batch size, or number of epochs, which are essential for reproducibility. |