Short and Sparse Deconvolution --- A Geometric Approach
Authors: Yenson Lau, Qing Qu, Han-Wen Kuo, Pengcheng Zhou, Yuqian Zhang, John Wright
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS |
| Researcher Affiliation | Academia | Yenson Lau Electrical Engineering Columbia University y.lau@columbia.edu Qing Qu Center for Data Science New York University qq213@nyu.edu Han-wen Kuo Electrical Engineering Columbia University hk2673@columbia.edu Pengcheng Zhou Department of Statistics Columbia University zhoupc2018@gmail.com Yuqian Zhang Electrical & Computer Engineering Rutgers University yqz.zhang@rutgers.edu John Wright Electrical Engineering Columbia University jw2966@columbia.edu |
| Pseudocode | Yes | Algorithm 1 Inertial Alternating Descent Method (i ADM) and Algorithm 2 Sa S-BD with homotopy continuation |
| Open Source Code | Yes | The code for implementations of our algorithms can be found online: https://github.com/qingqu06/sparse_deconvolution. |
| Open Datasets | Yes | Next, we test our method on real data10; Figures 5c and 5d demonstrate recovery of spike locations. 10Obtained at http://spikefinder.codeneuro.org. Here we will solve this task on the single-molecule localization microscopy (SMLM) benchmarking dataset11 via Sa SD... 11Data can be accessed at http://bigwww.epfl.ch/smlm/datasets/index.html. We consider frames video obtained via the two-photon calcium microscopy dataset from the Allen Institute for Brain Science13, shown in Figure 8. 13Obtained at http://observatory.brain-map.org/visualcoding/. |
| Dataset Splits | No | No explicit training/validation/test dataset splits (e.g., percentages, sample counts, or references to predefined splits) were found. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments were provided in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x) that are needed to replicate the experiments. |
| Experiment Setup | Yes | For each method, we deconvolve signals with p0 50, m 100p0, and θ p 3{4 0 for both coherent and incoherent a0. For both i ADM, i ADM with homotopy, and i PALM we set α 0.3. For homotopy, we set λp1q maxℓ|xsℓrap0qs, yy|, λ 0.3 ?p0λ, and δ 0.5. Furthermore we set η 0.5 or η 0.8 and for ADMM, we set the slack parameter to ρ 0.7 or ρ 0.5 for incoherent and coherent a0 respectively. We set x0 i.i.d. Bernoullipp 4{5 0 q P R104 with additive noise n i.i.d. Np0, 5 10 2q. For experiments in the main text, we set ε max ! |x|prn{ logpm{nqsq , 10 3) . |