Recovery of Sparse Signals from a Mixture of Linear Samples
Authors: Soumyabrata Pal, Arya Mazumdar
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our paper is theoretical, an important future work will be to find interesting use cases. |
| Researcher Affiliation | Academia | 1Computer Science Department at the University of Massachusetts Amherst, Amherst, MA01003, USA. Correspondence to: Arya Mazumdar <arya@cs.umass.edu>, Soumyabrata Pal <soumyabratap@umass.edu>. |
| Pseudocode | Yes | Algorithm 1 EM(x, σ, T ) Estimate the means x, β1 , x, β2 for a query x using EM algorithm... Algorithm 8 RECOVER UNKNOWN VECTORS(σ, γ) Recover the unknown vectors β1 and β2 |
| Open Source Code | No | The paper mentions 'Some proof of concept simulation results are also in the appendix.' but does not provide any links to open-source code for the described methodology or experiments. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using a specific dataset. Therefore, it does not mention training data availability. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with specific dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not mention specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers for its implementation. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or system-level training settings. |