Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Natural-Parameter Networks: A Class of Probabilistic Neural Networks
Authors: Hao Wang, Xingjian SHI, Dit-Yan Yeung
NeurIPS 2016 | Venue PDF | LLM Run Details | Input Tokens: 16,413 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,385 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world datasets show that NPN can achieve state-of-the-art performance. |
| Researcher Affiliation | Academia | Hao Wang, Xingjian Shi, Dit-Yan Yeung Hong Kong University of Science and Technology EMAIL |
| Pseudocode | Yes | Algorithm 1 Deep Nonlinear NPN |
| Open Source Code | No | The paper states, 'We use Matlab (with GPU) to implement NPN, AE variants, and the vanilla NN trained with dropout SGD (dropout NN).' However, no specific link or statement regarding the public availability of their NPN source code is provided. |
| Open Datasets | Yes | The MNIST digit dataset consists of 60,000 training images and 10,000 test images. Three real-world datasets, Citeulike-a, Citeulike-t, and ar Xiv, are used. The ο¬rst two datasets are from [22, 23], collected separately from Cite ULike in different ways to mimic different real-world settings. The third one is from ar Xiv as one of the SNAP datasets [15]. |
| Dataset Splits | Yes | We train the models with 50,000 images and use 10,000 images for validation. |
| Hardware Specification | No | The paper mentions using 'Matlab (with GPU)' but does not specify any particular GPU model, CPU, or other detailed hardware specifications for the experiments. |
| Software Dependencies | No | The paper mentions using 'Matlab', 'Theano library [2]', and 'MXNet [5]' but does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | No | The paper states, 'We implement BDK and NPN using the same hyperparameters as in [1] whenever possible.' and 'Gaussian priors are used for NPN (see the supplementary material for detailed hyperparameters).' It refers to external sources or supplementary material for hyperparameter details, rather than providing them explicitly in the main text. |