Restricted Boltzmann machines modeling human choice
Authors: Takayuki Osogami, Makoto Otsuka
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numerical experiments with real data of human choices suggest that we can train our choice model in such a way that it represents the typical phenomena of choice. Our experiments suggest that we can train the RBM choice model in such a way that it represents the typical choice phenomena. We show that the trained RBM choice model can then adequately predict real human choice on the means of transportation [2]. These experimental results constitute our third contribution and are presented in Section 4. |
| Researcher Affiliation | Industry | Takayuki Osogami IBM Research Tokyo osogami@jp.ibm.com Makoto Otsuka IBM Research Tokyo motsuka@ucla.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Here we use the dataset from [2], which is based on the survey conducted in Switzerland, where people are asked to choose a means of transportation from given options. [2] M. Bierlaire, K. Axhausen, and G. Abay. The acceptance of modal innovation: The case of Swissmetro. In Proceedings of the First Swiss Transportation Research Conference, March 2001. |
| Dataset Splits | No | The paper mentions using 'a subset...to train' and 'remaining dataset' for prediction, but does not explicitly provide specific dataset split information (percentages, sample counts) for training, validation, or testing, or refer to a predefined split. It indicates 18 test cases and evaluation using the 'entire training dataset'. |
| Hardware Specification | Yes | All of our experiments are run on a single core of a Windows PC with main memory of 8 GB and Core i5 CPU of 2.6 GHz. |
| Software Dependencies | No | The paper describes methods like 'stochastic gradient descent' but does not provide specific software names with version numbers for reproducibility. |
| Experiment Setup | Yes | We train the RBM choice model (or the MLM) by the use of discriminative training with stochastic gradient descent using the mini-batch of size 50 and the learning rate of η = 0.1 (see Appendix A.1). Each run of the evaluation uses the entire training dataset 50 times for training, and the evaluation is repeated five times by varying the initial values of the parameters. The elements of T and U are initialized independently with samples from the uniform distribution on [ 10/ p max(|I|, |K|), 10/ p max(|I|, |K|)], where |I| = 7 is the number of items under consideration, and |K| is the number of hidden nodes. The elements of b are initialized with samples from the uniform distribution on [ 1, 1]. |