The Generalized Lasso with Nonlinear Observations and Generative Priors
Authors: Zhaoqiang Liu, Jonathan Scarlett
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This is a theory paper primarily targeted at the research community. |
| Researcher Affiliation | Academia | Zhaoqiang Liu National University of Singapore dcslizha@nus.edu.sg; Jonathan Scarlett National University of Singapore scarlett@comp.nus.edu.sg |
| Pseudocode | No | The paper is theoretical and focuses on mathematical proofs and analyses; it does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments with datasets; therefore, no public datasets or access information are mentioned. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets; therefore, no dataset splits for training, validation, or testing are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments; therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments or provide code; therefore, no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not report on experiments; therefore, no experimental setup details like hyperparameters or training settings are provided. |