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..
Learning with a Wasserstein Loss
Authors: Charlie Frogner, Chiyuan Zhang, Hossein Mobahi, Mauricio Araya, Tomaso A. Poggio
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate this property on a real-data tag prediction problem, using the Yahoo Flickr Creative Commons dataset, outperforming a baseline that doesn t use the metric. |
| Researcher Affiliation | Collaboration | Charlie Frogner Chiyuan Zhang Center for Brains, Minds and Machines Massachusetts Institute of Technology EMAIL, EMAIL Hossein Mobahi CSAIL Massachusetts Institute of Technology EMAIL Mauricio Araya-Polo Shell International E & P, Inc. EMAIL Tomaso Poggio Center for Brains, Minds and Machines Massachusetts Institute of Technology EMAIL |
| Pseudocode | Yes | Algorithm 1 Gradient of the Wasserstein loss |
| Open Source Code | Yes | Code and data are available at http://cbcl.mit.edu/wasserstein. |
| Open Datasets | Yes | using the recently released Yahoo/Flickr Creative Commons 100M dataset [23]. ... The dataset used here is available at http://cbcl.mit.edu/wasserstein. |
| Dataset Splits | No | The paper mentions training and testing sets, but does not provide specific details about a validation set or explicit split percentages for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run its experiments, such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper mentions using "word2vec [24]" and "Mat Conv Net [25]" but does not specify version numbers for these software components. |
| Experiment Setup | Yes | We train a model independently for each value of p and plot the average predicted probabilities of the different digits on the test set in Figure 4. ... Specifically, we train a linear model by minimizing W p p + KL on the training set, where controls the relative weight of KL. |