Hierarchical Methods of Moments
Authors: Matteo Ruffini, Guillaume Rabusseau, Borja Balle
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on topic modeling show that our method outperforms previous tensor decomposition methods in terms of speed and model quality. |
| Researcher Affiliation | Collaboration | Matteo Ruffini Universitat Politècnica de Catalunya Guillaume Rabusseau Mc Gill University Borja Balle Amazon Research |
| Pseudocode | Yes | Algorithm 1 SIDIWO: Simultaneous Diagonalization based on Whitening and Optimization; Algorithm 2 Splitting a corpus into two parts |
| Open Source Code | Yes | The implementation of the described algorithms can be found a this link: https://github.com/mruffini/Hierarchical-Methods-of-Moments. |
| Open Datasets | Yes | We consider the full set of NIPS papers accepted between 1987 and 2015, containing n = 11, 463 papers [28]. [28] Valerio Perrone, Paul A Jenkins, Dario Spano, and Yee Whye Teh. Poisson random fields for dynamic feature models. ar Xiv preprint ar Xiv:1611.07460, 2016. |
| Dataset Splits | No | The paper describes generating synthetic data and processing real-world datasets for unsupervised hierarchical clustering, but it does not specify explicit training, validation, and test splits for model training or evaluation in the traditional sense. |
| Hardware Specification | Yes | All the experiments were run on a Mac Book Pro with an Intel Core i5 processor. |
| Software Dependencies | No | The experiments were performed in Python 2.7, using numpy library for linear algebra operations, with the exception of the implementation of the method from [22], for which we used the author s Matlab implementation. While software is mentioned, specific version numbers for numpy or Matlab are not provided. |
| Experiment Setup | Yes | We generate 400 samples according to this model and we iteratively run Algorithm 2 to create a hierarchical binary tree with 8 leafs. ...we keep the d = 3000 most frequent words as vocabulary and we iteratively run Algorithm 2 to create a binary tree of depth 4. ...relevance [29] of a word w 2 Cnode C is defined by r(w, Cnode) = λ log P[w|Cnode] + (1 λ) log P[w|C]) where the weight parameter is set to λ = 0.7 |