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.
Bidirectional Helmholtz Machines
Authors: Jorg Bornschein, Samira Shabanian, Asja Fischer, Yoshua Bengio
ICML 2016 | Venue PDF | LLM Run Details | Input Tokens: 16,835 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,511 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 | In this section we present experimental results obtained when applying the algorithm to various binary datasets. |
| Researcher Affiliation | Academia | J org Bornschein EMAIL Samira Shabanian EMAIL Asja Fischer EMAIL Yoshua Bengio EMAIL Dept. Computer Science and Operations Research, University of Montreal Canadian Institute for Advanced Research (CIFAR) |
| Pseudocode | Yes | Algorithm 1 Training p (x) using K importance samples |
| Open Source Code | Yes | Our implementation is available at https://github.com/jbornschein/bihm. |
| Open Datasets | Yes | We use the MNIST dataset that was binarized according to (Murray & Salakhutdinov, 2009) and which we downloaded in binarized form (Larochelle, 2011). |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly detail a separate validation dataset split with specific percentages or counts. |
| Hardware Specification | Yes | Estimating based on 10 million samples takes less than 2 minutes on a GTX980 GPU. |
| Software Dependencies | No | The paper mentions software like Theano and Blocks with citations, but does not specify their version numbers for reproducibility. |
| Experiment Setup | Yes | We train all models using Adam (Kingma & Ba, 2014) with a mini-batch size of 100. We initialize the weights according to Glorot & Bengio (2010), set the biases to -1, and use L1 regularization λ=10 3 on all the weights. Our implementation is available at https://github.com/jbornschein/bihm. We use a learning rate of 10 2 or 10 3 for all the experiments. |