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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Linear Time Sinkhorn Divergences using Positive Features
Authors: Meyer Scetbon, Marco Cuturi
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Figures 1,3 we plot the deviation from ground truth, defined as D := 100 ROT d ROT |ROT| + 100, and show the time-accuracy tradeoff for our proposed method RF, Nystrom Nys [3] and Sinkhorn Sin [16], for a range of regularization parameters |
| Researcher Affiliation | Collaboration | Meyer Scetbon CREST, ENSAE, Institut Polytechnique de Paris, EMAIL Marco Cuturi Google Brain, CREST, ENSAE, EMAIL |
| Pseudocode | Yes | Algorithm 1 Sinkhorn Inputs: K, a, b, δ, u repeat v b/KT u, u a/Kv until kv KT u bk1 < δ; Result: u, v |
| Open Source Code | Yes | The code is available at github.com/meyerscetbon/Linear Sinkhorn. |
| Open Datasets | Yes | We train our GAN models on a Tesla K80 GPU for 84 hours on two different datasets, namely CIFAR-10 dataset [35] and Celeb A dataset [38] |
| Dataset Splits | No | The paper uses datasets like CIFAR-10 and Celeb A but does not specify the exact training, validation, and test splits (e.g., percentages or sample counts) used for reproduction. |
| Hardware Specification | Yes | We train our GAN models on a Tesla K80 GPU for 84 hours on two different datasets, namely CIFAR-10 dataset [35] and Celeb A dataset [38] |
| Software Dependencies | No | The paper mentions the use of neural networks and GANs, implying common deep learning frameworks, but does not provide specific version numbers for software dependencies like Python, PyTorch, or TensorFlow. |
| Experiment Setup | Yes | More precisely we take the exact same functions used in [46, 36] to define g and fγ. Moreover, ' is the feature map associated to the Gaussian kernel defined in Lemma 1 where is initialised with a normal distribution. The number of random features considered has been fixed to be r = 600 in the following. The training procedure is the same as [27, 36] and consists in alterning nc optimisation steps to train the cost function c hγ and an optimisation step to train the generator g . (...) where we set the number of batches s = 7000, the regularization = 1, and the number of features r = 600. |