On the Challenges of Physical Implementations of RBMs
Authors: Vincent Dumoulin, Ian Goodfellow, Aaron Courville, Yoshua Bengio
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct software simulations to determine how harmful each of these restrictions is. Our simulations are based on the D-Wave Two computer, but the issues we investigate arise in most forms of physical computation. We used standard train / test split for both the MNIST (Le Cun et al. 1998) and Connect-4 and OCR Letters (Larochelle, Bengio, and Turian 2010) datasets. |
| Researcher Affiliation | Academia | Vincent Dumoulin and Ian J. Goodfellow and Aaron Courville and Yoshua Bengio D epartement d informatique et de recherche op erationnelle Universit e de Montr eeal Montr eal, QC H3C 3J7 {dumouliv,goodfeli,courvila}@iro.umontreal.ca yoshua.bengio@umontreal.ca |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that source code for the methodology is openly available. |
| Open Datasets | Yes | We used standard train / test split for both the MNIST (Le Cun et al. 1998) and Connect-4 and OCR Letters (Larochelle, Bengio, and Turian 2010) datasets. |
| Dataset Splits | Yes | We used standard train / test split for both the MNIST (Le Cun et al. 1998) and Connect-4 and OCR Letters (Larochelle, Bengio, and Turian 2010) datasets. |
| Hardware Specification | No | The paper mentions running simulations 'on a GPU' but does not provide specific details such as GPU model, CPU type, or memory, which are necessary for hardware specification. |
| Software Dependencies | No | The paper mentions 'PCD-15' as a training method but does not list specific software libraries or tools with version numbers (e.g., 'PyTorch 1.9', 'Python 3.8') that would be needed for replication. |
| Experiment Setup | Yes | All models were trained using PCD-15. Training examples were binarized every time they were presented by sampling from a Bernoulli distribution, such that the grayscale value in [0, 1] in the original image gives the probability of that pixel being a 1 in the binary image. Unless explicitly stated, all models were trained using the same hyperparameters. |