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..
Implicit Rank-Minimizing Autoencoder
Authors: Li Jing, Jure Zbontar, yann lecun
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate IRMAE s regularization behavior through a synthetic dataset and show that it learns good representation with a much smaller latent dimension. Then we demonstrate superior representation learning performance of our method against a standard deterministic autoencoder and comparable performance to a variational autoencoder on MNIST dataset and Celeb A dataset through a variety of generative tasks, including interpolation, sample generation from noise, PCA interpolation in low dimension, and a downstream classification task. |
| Researcher Affiliation | Industry | Li Jing Facebook AI Research New York EMAIL Jure Zbontar Facebook AI Research New York EMAIL Yann Le Cun Facebook AI Research New York EMAIL |
| Pseudocode | No | The paper describes the method and architecture but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We train our model on the MNIST dataset [15] and the Celeb A dataset [16]. |
| Dataset Splits | No | Each plot in Figures 2 and 4 depicts singular values (sorted from large to small) of the covariance matrix of the latent variables z corresponding to examples in the validation set. IRMAE l = 2 yields excellent reconstructions on validation set. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU, CPU models, memory) used for running the experiments. |
| Software Dependencies | No | Each model is trained with an Adam optimizer with a learning rate of 0.001. |
| Experiment Setup | Yes | We set the latent dimension to 128/512 for the two datasets, respectively. We use 8/4 extra linear matrices for regularization in IRMAE, respectively. ... Each model is trained with an Adam optimizer with a learning rate of 0.001. ... This MLP head has two linear layers of hidden dimension 128, with Re LU activation. |