On Efficient Low Distortion Ultrametric Embedding
Authors: Vincent Cohen-Addad, Karthik C. S., Guillaume Lagarde
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results We implemented our algorithm and performed experiments on three classic datasets (DIABETES, MICE, PENDIGITS) [...] Finally, we present empirical evaluation on classic machine learning datasets and show that the output of our algorithm is comparable to the output of the linkage algorithms while achieving a much faster running time. |
| Researcher Affiliation | Academia | 1Sorbonne Universit e, UPMC Univ Paris 06, CNRS, LIP6, Paris, France 2Department of Computer Science, Tel Aviv University, Israel 3La BRI, Universit e de Bordeaux, Bordeaux, France. |
| Pseudocode | No | The paper describes algorithms but does not include any explicitly labeled pseudocode blocks or algorithms. |
| Open Source Code | No | The paper states 'We implemented our algorithm' but does not provide any explicit statement about releasing the source code or a link to a repository. |
| Open Datasets | Yes | We present some experiments performed on three standard datasets: DIABETES (768 samples, 8 features), MICE (1080 samples, 77 features), PENDIGITS (10992 samples, 16 features) |
| Dataset Splits | No | The paper mentions using "classic datasets" and evaluating performance, but it does not specify any training, validation, or test dataset splits (e.g., percentages or counts). |
| Hardware Specification | No | The paper does not provide specific details on the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using the 'Scikit-learn library' and a 'C++ implementation' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | For a parameter γ fixed to 2.5, our results are as follows. |