Sequence Classification Restricted Boltzmann Machines With Gated Units

For the classification of sequential data, dynamic Bayesian networks and recurrent neural networks (RNNs) are the preferred models. While the former can explicitly model the temporal dependences between the variables, and the latter have the capability of learning representations. The recurrent temporal restricted Boltzmann machine (RTRBM) is a model that combines these two features. However, learning and inference in RTRBMs can be difficult because of the exponential nature of its gradient computations when maximizing log likelihoods. In this article, first, we address this intractability by optimizing a conditional rather than a joint probability distribution when performing sequence classification. This results in the ``sequence classification restricted Boltzmann machine'' (SCRBM). Second, we introduce gated SCRBMs (gSCRBMs), which use an information processing gate, as an integration of SCRBMs with long short-term memory (LSTM) models. In the experiments reported in this article, we evaluate the proposed models on optical character recognition, chunking, and multiresident activity recognition in smart homes. The experimental results show that gSCRBMs achieve the performance comparable to that of the state of the art in all three tasks. gSCRBMs require far fewer parameters in comparison with other recurrent networks with memory gates, in particular, LSTMs and gated recurrent units (GRUs)

Integration of Adjectives to Learn Grammatical Gender for Object Categorization in Urdu for Balti Speakers

This quantitative research is an attempt to explore the possibilities to decrease the level of difficulty for Balti language speakers in object categorization while they learn Urdu language as their L2. Purpose of this research is to know that how chunking technique can help the Balti speakers remember the grammatical gender of different inanimate objects while keeping in mind their adjective-noun pairs. This is an experimental study and population for this research is comprised of all Balti language speakers living in Lahore. Simple random sampling technique is used to select the sample and teaching sessions are conducted by the researcher to teach experimental and controlled groups. Findings of this study show that chunking technique is beneficial to be used for L2 learners of Urdu language especially if their L1 doesn’t have the concept of grammatical gender. In conclusion, it is recommended through this research to apply the same technique of linking adjectives with nouns in pedagogical approach for the Balti speakers who intend learn Urdu as their L2 with the help of their curriculum