Efficient First-Order Algorithms for Adaptive Signal Denoising
Authors: Dmitrii Ostrovskii, Zaid Harchaoui
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed procedures and their analysis are illustrated on a simulated data benchmark.In Section 5, we present numerical experiments on simulated data which complement our theoretical analysis |
| Researcher Affiliation | Academia | 1SIERRA Project-Team, INRIA Paris, Paris, France 2Department of Statistics, University of Washington, Seattle, USA. |
| Pseudocode | Yes | Algorithm 1 Fast Gradient Method; Algorithm 2 Composite Mirror Prox |
| Open Source Code | Yes | The code reproducing all our experiments is available online at https://github.com/ostrodmit/AlgoRec. |
| Open Datasets | No | The paper describes scenarios ('Random-s' and 'Coherent-s') for generating simulated data rather than using or providing access to a pre-existing public dataset. |
| Dataset Splits | No | The paper describes simulation experiments but does not specify dataset splits such as training, validation, or test sets in the context of machine learning model training. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | Yes | The MOSEK optimization toolbox for MATLAB manual. Version 7.0, 2013. |
| Experiment Setup | No | The paper describes the generation of synthetic signals and parameters like 'n = 100' and 'SNR', but it does not specify typical experimental setup details for machine learning models such as learning rates, batch sizes, or optimizer settings. |