Bidirectional Helmholtz Machines
Authors: Jorg Bornschein, Samira Shabanian, Asja Fischer, Yoshua Bengio
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 BORNJ@IRO.UMONTREAL.CA Samira Shabanian SHABANIS@IRO.UMONTREAL.CA Asja Fischer FISCHER@IRO.UMONTREAL.CA Yoshua Bengio BENGIOY@IRO.UMONTREAL.CA 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. |