Hierarchical Optimal Transport for Multimodal Distribution Alignment
Authors: John Lee, Max Dabagia, Eva Dyer, Christopher Rozell
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply this method to synthetic datasets that model data as mixtures of low-rank Gaussians and study the impact that different geometric properties of the data have on alignment. Next, we applied our approach to a neural decoding application where the goal is to predict movement directions and instantaneous velocities from populations of neurons in the macaque primary motor cortex. Our results demonstrate that when clustered structure exists in datasets, and is consistent across trials or time points, a hierarchical alignment strategy that leverages such structure can provide significant improvements in cross-domain alignment. |
| Researcher Affiliation | Academia | School of Electrical and Computer Engineering, Coulter Department of Biomedical Engineering Georgia Institute of Technology, Atlanta, GA, 30332 USA {john.lee, maxdabagia, evadyer, crozell}@gatech.edu |
| Pseudocode | Yes | Algorithm 1 Hierarchical Wasserstein Alignment (Hi WA) Algorithm |
| Open Source Code | Yes | MATLAB code can be found at https://github.com/siplab-gt/hiwa-matlab. Neural datasets and Python code are provided at http://nerdslab.github.io/neuralign |
| Open Datasets | Yes | Neural datasets and Python code are provided at http://nerdslab.github.io/neuralign |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits, nor does it refer to predefined splits with citations or detailed splitting methodologies. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments, such as specific GPU or CPU models, or cloud resources with specifications. |
| Software Dependencies | No | The paper mentions 'MATLAB code' and 'Python code' but does not provide specific version numbers for these or any other key software components, libraries, or solvers. |
| Experiment Setup | No | The paper describes general parameters of the Hi WA algorithm (entropic parameters γ1, γ2 > 0, ADMM parameter µ > 0) and dataset characteristics, but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations used in its experiments. |