Echo-State Conditional Restricted Boltzmann Machines
Authors: Sotirios Chatzis
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our methods to sequential data modeling and classification experiments using public datasets. As we experimentally demonstrate, our methods outperform alternative RBM-based approaches, as well as other stateof-the-art methods, such as CRFs, in both data modeling and classification applications from diverse domains. |
| Researcher Affiliation | Academia | Sotirios P. Chatzis Department of Electrical Engineering, Computer Engineering, and Informatics Cyprus University of Technology Limassol 3603, Cyprus soteri0s@mac.com |
| Pseudocode | No | The paper describes algorithmic steps in text and bullet points but does not include structured pseudocode or algorithm blocks with formal labels. |
| Open Source Code | No | The paper does not provide concrete access to source code, such as a specific repository link, an explicit code release statement, or code in supplementary materials, for the methodology described in this paper. |
| Open Datasets | Yes | our experiments are based on the dataset described in (Ni, Wang, and Moulin 2011). |
| Dataset Splits | Yes | We use cross-validation in the following fashion: in each cycle, we use 15 randomly selected video sequences to perform training, and keep the rest 20 for testing. We have recorded 4 demonstrations and used 3 for training and 1 for testing purposes. means and standard deviations obtained by application of leave-one-out crossvalidation |
| Hardware Specification | No | The paper mentions devices for data collection (Kinect TM device) and demonstration (NAO robot) but does not provide specific hardware details (like GPU/CPU models, processor types, or memory) used for running the computational experiments. |
| Software Dependencies | No | The paper mentions various algorithms and methods (e.g., CD-k, LM, i SVM, CRBM) but does not provide specific software names with version numbers for ancillary software dependencies. |
| Experiment Setup | Yes | In all our experiments, the CD-k algorithm is performed with k = 10; all parameters use a gradient ascent learning rate equal to 10 3, except for the autoregressive weights of the im CRBM method, where the learning rate is equal to 10 5. A momentum term is also used: 0.9 of the previously accumulated gradient is added to the current gradient. We use hyperbolic-tangent reservoir neurons, h( ) tanh( ); the reservoir spectral radius is set equal to 0.95. |