Trimmed Density Ratio Estimation
Authors: Song Liu, Akiko Takeda, Taiji Suzuki, Kenji Fukumizu
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments are conducted to verify the effectiveness of the estimator. and 6 Experiments |
| Researcher Affiliation | Academia | Song Liu University of Bristol song.liu@bristol.ac.uk Akiko Takeda The Institute of Statistical Mathematics, AIP, RIKEN, atakeda@ism.ac.jp Taiji Suzuki University of Tokyo, Sakigake (PRESTO), JST, AIP, RIKEN, taiji@mist.i.u-tokyo.ac.jp Kenji Fukumizu The Institute of Statistical Mathematics, fukumizu@ism.ac.jp |
| Pseudocode | Yes | Algorithm 1 Gradient Ascent and Trimming |
| Open Source Code | Yes | Code can be found at http://allmodelsarewrong.org/software.html |
| Open Datasets | No | No specific link, DOI, repository name, or formal citation to a publicly available or open dataset was provided. The datasets described appear to be custom-collected for the experiments, e.g., 'We collect four images (see Figure 3a)...' |
| Dataset Splits | No | No specific details on dataset splits (e.g., percentages, sample counts for training, validation, or test sets, or citations to standard splits) were found. |
| Hardware Specification | No | No specific hardware details (like GPU models, CPU models, or memory specifications) used for running experiments were mentioned. A general mention of 'GPU acceleration' does not suffice. |
| Software Dependencies | No | The paper mentions 'Tensorflow2' but does not provide a specific version number or any other software dependencies with version information. |
| Experiment Setup | Yes | To induce sparsity, we set R( ) = Pd i,j=1,i j | i,j| and fix λ = 0.0938. Then run DRE and TRimmed-DRE to learn the sparse differential precision matrix... We fix ν in TR-DRE to 90% and compare the performance of DRE and TR-DRE... |