Neurally Augmented ALISTA
Authors: Freya Behrens, Jonathan Sauder, Peter Jung
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate NA-ALISTA in a sparse reconstruction task and compare it against ALISTA (Liu et al., 2019), ALISTA-AT (Kim & Park, 2020), AGLISTA (Wu et al., 2020), as well as the classical ISTA (Daubechies et al., 2003) and FISTA (Beck & Teboulle, 2009). To emphasize a fair and reproducible comparison between the models, the code for all experiments listed is available on Git Hub. |
| Researcher Affiliation | Academia | Freya Behrens1, , Jonathan Sauder1, , Peter Jung1,2, Communications and Information Theory Chair, Technical University of Berlin1, Data Science in Earth Observation, Technical University of Munich2 |
| Pseudocode | Yes | Algorithm 1: Neurally Augmented ALISTA |
| Open Source Code | Yes | To emphasize a fair and reproducible comparison between the models, the code for all experiments listed is available on Git Hub 2. https://github.com/feeds/na-alista |
| Open Datasets | No | The paper describes generating synthetic data based on random variables and distributions, and using a statistical model for real-world channel estimation. It does not provide concrete access information (link, DOI, specific repository, or formal citation with authors/year) for a pre-existing publicly available dataset. |
| Dataset Splits | No | The paper mentions a 'test set of 10000 samples is fixed before training' and 'We train all algorithms for 400 epochs', but does not explicitly state the use or size of a separate validation set or the specific splits for training, validation, and testing. |
| Hardware Specification | Yes | Computations were run on a system with a NVIDIA Tesla P100 GPU and Intel(R) Xeon(R), with the GPU enabled (a) and CPU only (b). |
| Software Dependencies | No | The paper mentions the use of 'The Adam optimizer' and 'support selection' but does not provide specific version numbers for any software components or libraries. |
| Experiment Setup | Yes | When not otherwise indicated we use the following settings for experiments and algorithms: M = 250, N = 1000, S = 50, K = 16, H = 128, and y = Φx + z with additive white Gaussian noise z with a signal to noise ratio SNR:= E( Φx 2 2)/E( z 2 2) = 40d B. We train all algorithms for 400 epochs, with each epoch containing 50,000 sparse vectors with a batch size of 512. |