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].
Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons
Authors: Nima Anari, Constantinos Daskalakis, Wolfgang Maass, Christos Papadimitriou, Amin Saberi, Santosh Vempala
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We analyze linear independence of rank one tensors produced by tensor powers of randomly perturbed vectors. This enables ef๏ฌcient decomposition of sums of high-order tensors. Our analysis builds upon Bhaskara et al. [3] but allows for a wider range of perturbation models, including discrete ones. We give an application to recovering assemblies of neurons. |
| Researcher Affiliation | Academia | Nima Anari Computer Science Stanford University EMAIL Daskalakis MIT EMAIL Wolfgang Maass Theoretical Computer Science Graz University of Technology EMAIL Christos H. Papadimitriou Computer Science Columbia University EMAIL Amin Saberi MS&E Stanford University EMAIL Santosh Vempala Computer Science Georgia Tech EMAIL |
| Pseudocode | No | The paper describes algorithms conceptually and mathematically, but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code for its methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with datasets, so it does not refer to public or open datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with datasets, so it does not specify dataset splits for reproduction. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not conduct experiments, therefore no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not conduct experiments, therefore no experimental setup details such as hyperparameters are provided. |