Trimmed Density Ratio Estimation
Authors: Song Liu, Akiko Takeda, Taiji Suzuki, Kenji Fukumizu
NeurIPS 2017 | Venue 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 EMAIL Akiko Takeda The Institute of Statistical Mathematics, AIP, RIKEN, EMAIL Taiji Suzuki University of Tokyo, Sakigake (PRESTO), JST, AIP, RIKEN, EMAIL Kenji Fukumizu The Institute of Statistical Mathematics, EMAIL |
| 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... |