Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit
Authors: Karin C Knudson, Jacob Yates, Alexander Huk, Jonathan W Pillow
NeurIPS 2014 | Venue PDF | 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 EMAIL Jacob L. Yates Department of Neuroscience The University of Texas at Austin EMAIL Alexander C. Huk Center for Perceptual Systems Departments of Psychology & Neuroscience The University of Texas at Austin EMAIL Jonathan W. Pillow Princeton Neuroscience Institute and Department of Psychology Princeton University EMAIL |
| 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. |