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 [1].
Associative Memory Using Dictionary Learning and Expander Decoding
Authors: Arya Mazumdar, Ankit Singh Rawat
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Though our main contribution is theoretical, in this section we evaluate the proposed associative memory on synthetic dataset to verify if our methods works. Only a representative figure is presented here (Fig. 2). |
| Researcher Affiliation | Academia | Arya Mazumdar College of Information & Computer Science University of Massachusetts Amherst EMAIL Ankit Singh Rawat Research Laboratory of Electronics Massachusetts Institute of Technology EMAIL |
| Pseudocode | Yes | Figure 1: Recovery algorithm for sparse vector from expander graphs based measurement matrix (Jafarpour et al. 2009). |
| Open Source Code | No | The paper does not contain any statement about making the source code available or provide a link to a code repository. |
| Open Datasets | No | The paper uses synthetic datasets which are generated for the experiments, but does not provide access information (e.g., link, DOI, or citation for a public dataset). |
| Dataset Splits | No | The paper does not specify explicit training, validation, or test dataset splits. The data is synthetically generated for each run, with details provided for the generation process. |
| Hardware Specification | No | The paper does not explicitly describe any specific hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers that would be necessary to replicate the experiments. |
| Experiment Setup | Yes | We consider three sets of system parameters (m, n, d) for the dataset to be stored. For each set of parameters, we first generate an m n random matrix B according to the sparse-sub-Gaussian model (cf. Sec. 2.1). Each non-zero entry of the matrix B is drawn uniformly at random from the set { 1, 2, 3}. ... For a fixed number E of errors, we generate 100 error vectors e Rn with the number of non-zero entries in each error vector equal to E. The non-zero entries in these vectors are uniformly generated from the set { 1, . . . , 4}. |