Boosted Generative Models
Authors: Aditya Grover, Stefano Ermon
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments are designed to demonstrate the superiority of the proposed boosting meta-algorithms on a wide variety of generative models and tasks. |
| Researcher Affiliation | Academia | Aditya Grover, Stefano Ermon Department of Computer Science Stanford University {adityag, ermon}@cs.stanford.edu |
| Pseudocode | Yes | Algorithm 1 Gen BGM(X = {xi}m i=1, T rounds) and Algorithm 2 Disc BGM(X = {xi}m i=1, T rounds, f-div) |
| Open Source Code | Yes | A reference implementation of the boosting meta-algorithms is available at https://github.com/ermongroup/bgm. |
| Open Datasets | Yes | binarized MNIST dataset of handwritten digits (Le Cun, Cortes, and Burges 2010). http://yann. lecun. com/exdb/mnist. benchmark datasets (Van Haaren and Davis 2012). |
| Dataset Splits | Yes | We observe 1, 000 training samples drawn independently from the data distribution... The test set contains 1, 000 samples from the same distribution. Model weights are chosen based on cross-validation. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, or memory) used for running experiments were provided in the paper. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' and 'variational autoencoders (VAE)' and 'convolutional neural network (CNN)' but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | The classifiers for Disc BGM are multi-layer perceptrons with two hidden layers of 100 units each and Re LU activations, trained to maximize f-divergences corresponding to the negative cross-entropy (NCE) and Hellinger distance (HD) using the Adam optimizer (Kingma and Welling 2014). We set T = 2 rounds for additive boosting and Gen BGM. VAE hidden layer architecture given in parenthesis (e.g., 200-100, 200-100-100, 300-100, 100-50). |