Global Geometry of Multichannel Sparse Blind Deconvolution on the Sphere
Authors: Yanjun Li, Yoram Bresler
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical findings are complemented by numerical experiments, which demonstrate superior performance of the proposed approach over the previous methods. |
| Researcher Affiliation | Academia | Yanjun Li CSL and Department of ECE University of Illinois Urbana-Champaign yli145@illinois.eduYoram Bresler CSL and Department of ECE University of Illinois Urbana-Champaign ybresler@illinois.edu |
| Pseudocode | No | The paper describes the iterative update rule for manifold gradient descent as a mathematical formula: 'h(t+1) = A(h(t)) := PSn 1 h(t) γ b L(h(t)) .' However, it does not present this or any other procedure in a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We use a publicly available microtubule dataset [28]. [28] E. A. Mukamel, H. Babcock, and X. Zhuang, Statistical deconvolution for superresolution fluorescence microscopy, Biophysical journal, vol. 102, no. 10, pp. 2391 2400, 2012. |
| Dataset Splits | No | The paper does not provide specific details on how datasets (synthetic or real) were split into training, validation, or test sets, nor does it refer to standard predefined splits for replication. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, cloud instances) used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., specific programming languages, libraries, or frameworks used for implementation or data processing). |
| Experiment Setup | Yes | In all experiments, we run manifold gradient descent for T = 100 iterations, with a fixed step size of γ = 0.1. |