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 | Conference PDF | Archive PDF | Plain Text | 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 efficient 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 anari@cs.stanford.eduConstantinos Daskalakis MIT costis@csail.mit.edu Wolfgang Maass Theoretical Computer Science Graz University of Technology maass@igi.tugraz.at Christos H. Papadimitriou Computer Science Columbia University christos@cs.columbia.edu Amin Saberi MS&E Stanford University saberi@stanford.edu Santosh Vempala Computer Science Georgia Tech vempala@gatech.edu |
| 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. |