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..
Wasserstein Dependency Measure for Representation Learning
Authors: Sherjil Ozair, Corey Lynch, Yoshua Bengio, Aaron van den Oord, Sergey Levine, Pierre Sermanet
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this work, we empirically demonstrate that mutual information-based representation learning approaches do fail to learn complete representations on a number of designed and real-world tasks. |
| Researcher Affiliation | Collaboration | Sherjil Ozair Mila, Université de Montréal Corey Lynch Google Brain Yoshua Bengio Mila, Université de Montréal Aäron van den Oord Deepmind Sergey Levine Google Brain Pierre Sermanet Google Brain |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements or links to open-source code for the methodology described. |
| Open Datasets | Yes | We used the Omniglot dataset [31] as a base dataset to construct Spatial Multi Omniglot and Stacked Multi Omniglot. ... Shapes3D [27] (Figure 1) is a dataset of images of a single object in a room. ... Celeb A [34] is a dataset consisting of celebrity faces. |
| Dataset Splits | No | The paper mentions a fixed training dataset size (50,000 samples) but does not provide specific details for a separate validation split (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., libraries, frameworks, or specific tools). |
| Experiment Setup | No | While the paper discusses the effect of minibatch size, it does not provide concrete hyperparameter values or detailed system-level training configurations needed to reproduce the experiment setup. |