A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices
Authors: Rudrasis Chakraborty, Chun-Hao Yang, Xingjian Zhen, Monami Banerjee, Derek Archer, David Vaillancourt, Vikas Singh, Baba Vemuri
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform numerical experiments demonstrating competitive performance with state of the art methods but with significantly fewer parameters. We also show applications to a statistical analysis task in brain imaging, a regime where deep neural network models have only been utilized in limited ways. |
| Researcher Affiliation | Academia | University of Florida, Gainesville, USA University of Wisconsin Madison, USA |
| Pseudocode | No | The paper describes mathematical equations for its model but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Our code is available from https://goo.gl/Sf Aez S. |
| Open Datasets | Yes | We used the Moving MNIST data as generated in [57]. For this experiment we performed 2 and 3 classes classification experiment. In each class, we generated 1000 sequences each of length 20. ... We performed an action classification experiment on UCF11 dataset [43]. ... The data pool consists of d MRI (human) brain scans acquired from 50 PD patients and 44 CON healthy controls. |
| Dataset Splits | Yes | In each class, we generated 1000 sequences each of length 20... We report the mean and standard deviation of the 10fold testing accuracy. ... All the models achieve > 90% training accuracy. ... Then we perform permutation testing for each tract as follows (i) randomly permute the class labels of the subjects and learn m PD and m CON models for each of the new group. |
| Hardware Specification | Yes | All images were collected using a 3.0T MR scanner (Philips Achieva) and 32-channel quadrature volume head coil. |
| Software Dependencies | No | The paper mentions using 'FSL [8] software' but does not specify a version number for it or any other ancillary software. |
| Experiment Setup | Yes | For the convolution layer, we chose the kernel size to be 5 5 and the input and output channels to be 5 and 10 respectively... The number of output units for LSTM is set to 10 and the number of statistics for SRU is set to 80. ... SPD-SRU takes 75 epochs to converge to the reported results... The parameters of the diffusion imaging acquisition sequence were: gradient directions = 64, b-values = 0/1000 s/mm2, repetition time =7748 ms, echo time = 86 ms, flip angle = 90 , field of view = 224 224 mm, matrix size = 112 112, number of contiguous axial slices = 60 and SENSE factor P = 2. |