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..
Deep Homogeneous Mixture Models: Representation, Separation, and Approximation
Authors: Priyank Jaini, Pascal Poupart, Yaoliang Yu
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on both synthetic and real datasets confirm the benefits of depth in density estimation. |
| Researcher Affiliation | Academia | Priyank Jaini Department of Computer Science & Waterloo AI Institute University of Waterloo EMAIL Pascal Poupart University of Waterloo, Vector Institute & Waterloo AI Institute EMAIL Yaoliang Yu Department of Computer Science & Waterloo AI Institute University of Waterloo EMAIL |
| Pseudocode | No | The paper describes algorithms but does not provide pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper mentions adapting code from another work ('HT-TMM') but does not state that its own developed code ('SPN-CG') is open-source or provide a link to it. |
| Open Datasets | Yes | We perform experiments on MNIST [15] for digit classification and small NORB [16] for 3D object recognition |
| Dataset Splits | No | The paper mentions using MNIST and NORB datasets and adapting experiments from [26], but it does not explicitly state the training, validation, or test splits used within its text. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using an 'Adam SGD variant' for training but does not specify any software packages, libraries, or their version numbers. |
| Experiment Setup | Yes | For each iteration, we train the network using an Adam SGD variant with a base learning rate of 0.03 and momentum parameters β1 = β2 = 0.9. For each added network structure, we train the model for 22,000 iterations for MNIST and 40,000 for NORB. |