Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit
Authors: Karin C Knudson, Jacob Yates, Alexander Huk, Jonathan W Pillow
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test the resulting method, which we call Continuous Orthogonal Matching Pursuit (COMP), on simulated and neural data, where it shows gains over CBP in both speed and accuracy. |
| Researcher Affiliation | Academia | Karin C. Knudson Department of Mathematics The University of Texas at Austin kknudson@math.utexas.edu Jacob L. Yates Department of Neuroscience The University of Texas at Austin jlyates@utexas.edu Alexander C. Huk Center for Perceptual Systems Departments of Psychology & Neuroscience The University of Texas at Austin huk@utexas.edu Jonathan W. Pillow Princeton Neuroscience Institute and Department of Psychology Princeton University pillow@princeton.edu |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block was found. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper uses simulated data, which is generated internally, and neural data collected by the authors without providing public access information. |
| Dataset Splits | No | The paper describes using simulated and neural data and evaluating performance directly on it, but does not specify explicit train/validation/test dataset splits in the conventional sense. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, processor types) used for running experiments were mentioned. |
| Software Dependencies | No | The paper mentions "the cvx package for MATLAB" but does not specify a version number in the main text directly in relation to the authors' implementation. Reference [7] mentions "version 2.0" for CVX, but this is not explicitly stated as a dependency with its version number for the authors' experimental setup. |
| Experiment Setup | Yes | Throughout, we use K = 3, since the polar basis requires 3 basis vectors per bin. We categorize hits, false positive and misses based on whether a time shift estimate is within a threshold of ϵ = 1 of the true value. ... we also impose a thresholding step after recovery with CBP, discarding any recovered waveforms with amplitude less than .3. |