An Architecture for Deep, Hierarchical Generative Models
Authors: Philip Bachman
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our approach achieves state-of-the-art performance on standard image modelling benchmarks, can expose latent class structure in the absence of label information, and can provide convincing imputations of occluded regions in natural images. |
| Researcher Affiliation | Industry | Philip Bachman phil.bachman@maluuba.com Maluuba Research |
| Pseudocode | No | The paper describes procedures in text, but does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | Yes | Exhaustive descriptions of the modules can be found in our code at: https://github.com/Philip-Bachman/Mat Nets-NIPS. |
| Open Datasets | Yes | We measured quantitative performance of Mat Nets on three datasets: MNIST, Omniglot [13], and CIFAR 10 [12]. We used the 28x28 version of Omniglot described in [2], which can be found at: https://github.com/yburda/iwae. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for a validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper states, "Complete hyperparameters for model architecture and optimization can be found in the code at https://github.com/Philip-Bachman/Mat Nets-NIPS.", but does not provide specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) directly in the main text. |