Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Neurally Augmented ALISTA
Authors: Freya Behrens, Jonathan Sauder, Peter Jung
ICLR 2021 | Venue PDF | 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. |