Titlebook: Artificial Intelligence XXXIV; 37th SGAI Internatio Max Bramer,Miltos Petridis Conference proceedings 2017 Springer International Publishin

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Quantization Error-Based Regularization in Neural Networksr and memory footprint are restricted in embedded computing, precision quantization of numerical representations, such as fixed-point, binary, and logarithmic, are commonly used for higher computing efficiency. The main problem of quantization is accuracy degradation due to its lower numerical repre

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Knowledge Transfer in Neural Language Modelsls have proved challenging to scale into and out of various domains. In this paper we discuss the limitations of current approaches and explore if transferring human knowledge into a neural language model could improve performance in an deep learning setting. We approach this by constructing gazette

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Programming Without Program or How to Program in Natural Language Utterancess, in natural language utterances; engineers are afforded their own concepts and associated conversations. This paper shows how this can be turned in on itself, programming the interpretation of utterances, itself, purely through utterance.

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Knowledge Transfer in Neural Language Modelsers from existing public resources. We demonstrate that leveraging existing knowledge we can increase performance and train such networks faster. We argue a case for further research into leveraging pre-existing domain knowledge and engineering resources to train neural models.

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