Superset Technique for Approximate Recovery in One-Bit Compressed Sensing
Authors: Larkin Flodin, Venkata Gandikota, Arya Mazumdar
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present some empirical results relating to the use of our superset technique in approximate vector recovery for real-valued signals. |
| Researcher Affiliation | Academia | Larkin Flodin University of Massachusetts Amherst Amherst, MA 01003 lflodin@cs.umass.edu Venkata Gandikota University of Massachusetts Amherst Amherst, MA 01003 gandikota.venkata@gmail.com Arya Mazumdar University of Massachusetts Amherst Amherst, MA 01003 arya@cs.umass.edu |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block was found in the paper. |
| Open Source Code | No | The paper does not provide explicit statements about releasing source code for the described methodology nor does it include direct links to a code repository. |
| Open Datasets | No | The paper describes how random signals are generated for experiments but does not provide access information for a publicly available or open dataset that was used. |
| Dataset Splits | No | The paper does not explicitly provide details about training, validation, or test dataset splits; it describes generating random signals for experiments. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library or solver names like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | For the solid lines in Figure 1 labeled 4k log n Superset, we again performed 500 trials for each value of (n, m, k) where in each trial we generated a measurement matrix M = rows in total. Each entry of M (1) is a Bernoulli random variable that takes value 1 with probability 1 k+1 and value 0 with probability k k+1;...The entries of M (2) are drawn from N(0, 1). We use a standard group testing decoding (i.e., remove any coordinates that appear in a test with result 0) to determine a superset based on y1 = sign(M (1)x), then use BIHT (again run either until convergence or 1000 iterations) to reconstruct x within the superset using the measurement results y2 = sign(M (2)x). The number of rows in M (1) is taken to be m1 = 4k log10(n) based on the fact that with high probability Ck log n rows for some constant C should be sufficient to recover an O (k)-sized superset, and the remainder m2 = (m m1) of the measurements are used in M (2). |