Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity
Authors: Gonçalo Correia, Vlad Niculae, Wilker Aziz, André Martins
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report successful results in three tasks covering a range of latent variable modeling applications: a semisupervised deep generative model, a latent communication game, and a generative model with a bit-vector latent representation. and 5 Experimental Analysis We next demonstrate the applicability of our proposed strategies by tackling three tasks: a deep generative model with semisupervision ( 5.1), an emergent communication two-player game over a discrete channel ( 5.2), and a variational autoencoder with latent binary factors ( 5.3). |
| Researcher Affiliation | Collaboration | Gonçalo M. Correiaä goncalo.correia@lx.it.pt Vlad Niculaeæ vlad@vene.ro Wilker Azizå w.aziz@uva.nl André F. T. Martinsä Èã andre.t.martins@tecnico.ulisboa.pt äInstituto de Telecomunicações, Lisbon, Portugal ÈLUMLIS (Lisbon ELLIS Unit), Instituto Superior Técnico, Lisbon, Portugal ãUnbabel, Lisbon, Portugal åILLC, University of Amsterdam, The Netherlands æIv I, University of Amsterdam, The Netherlands |
| Pseudocode | No | The paper describes algorithms and procedures in text (e.g., "The active set algorithm for Sparse MAP"), and Appendix B details "The Active Set Algorithm for Sparse MAP", but it does not present structured pseudocode or a formally labeled algorithm block. |
| Open Source Code | Yes | Code is publicly available at https://github.com/deep-spin/sparse-marginalization-lvm |
| Open Datasets | Yes | Data and architecture. We evaluate this model on the MNIST dataset [31], using 10% of labeled data, treating the remaining data as unlabeled. and We use Fashion-MNIST [42], consisting of 256-level grayscale images x {0, 1, . . . , 255}28 28. |
| Dataset Splits | No | The paper mentions using 10% labeled data for the semisupervised VAE task, and discusses training epochs, but does not provide specific train/validation/test dataset splits (percentages or counts) for reproducibility. |
| 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 mentions using PyTorch [62] for implementation, but it does not specify version numbers for PyTorch or any other software dependencies needed to reproduce the experiments. |
| Experiment Setup | Yes | We describe any further architecture and hyperparameter details in App. E. and within the experimental sections, details like For top-k sparsemax, we choose k = 10. and we used b = D 2 are mentioned. Appendix E details include: Each model was trained for 200 epochs. and All methods are trained for 500 epochs. |