Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Debiased Sinkhorn barycenters
Authors: Hicham Janati, Marco Cuturi, Alexandre Gramfort
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we illustrate the reduced blurring and the computational advantage on various applications. |
| Researcher Affiliation | Collaboration | 1Inria Saclay, France 2CREST-ENSAE, France 3Google Research, Brain team, France. |
| Pseudocode | Yes | Algorithm 1 Debiased Sinkhorn Barycenter |
| Open Source Code | Yes | Python code can be found at https://github.com/hichamjanati/debiased-ot-barycenters. |
| Open Datasets | Yes | We take 500 samples of the MNIST dataset (Le Cun & Cortes, 2010) |
| Dataset Splits | Yes | We select 10% of the dataset (a subset of 50 images; ergo K=50) at random as our learning dictionary A and compute the barycentric coordinates of the remaining 90% subset denoted as D. ... We train a random forest classi๏ฌer using the Scikit-learn library (Pedregosa et al., 2011) on this learned embedding) and compute a 10-fold cross-validation. |
| Hardware Specification | Yes | All 6 barycenters were computed on a laptop with an Intel Core i5 3.1 GHz Processor. |
| Software Dependencies | No | The paper mentions 'Scikit-learn library (Pedregosa et al., 2011)' and 'pyTorch library (Paszke et al., 2017)' but does not specify their version numbers. |
| Experiment Setup | Yes | We set the cost matrix C to the squared Euclidean distance on the unit square and set " = 0.002. We use the same termination criterion for all methods based on a maximum relative change of the barycenters set to 10 5. ... We set the cost matric C to the squared Euclidean distance on the unit cube and set " = 0.01. ... We select 10% of the dataset (a subset of 50 images; ergo K=50) at random as our learning dictionary A. |