Learning with a Wasserstein Loss
Authors: Charlie Frogner, Chiyuan Zhang, Hossein Mobahi, Mauricio Araya, Tomaso A. Poggio
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | 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 frogner@mit.edu, chiyuan@mit.edu Hossein Mobahi CSAIL Massachusetts Institute of Technology hmobahi@csail.mit.edu Mauricio Araya-Polo Shell International E & P, Inc. Mauricio.Araya@shell.com Tomaso Poggio Center for Brains, Minds and Machines Massachusetts Institute of Technology tp@ai.mit.edu |
| 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. |