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Title: Unsupervised multimodal neural networks
Author: Nyamapfene, Abel
ISNI:       0000 0001 3451 012X
Awarding Body: University of Surrey
Current Institution: University of Surrey
Date of Award: 2006
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We extend the in-situ Hebbian-linked SOMs network by Miikkulainen to come up with two unsupervised neural networks that learn the mapping between the individual modes of a multimodal dataset. The first network, the single-pass Hebbian linked SOMs network, extends the in-situ Hebbian-linked SOMs network by enabling the Hebbian link weights to be computed through one- shot learning. The second network, a modified counter propagation network, extends the unsupervised learning of crossmodal mappings by making it possible for only one self-organising map to implement the crossmodal mapping. The two proposed networks each have a smaller computation time and achieve lower crossmodal mean squared errors than the in-situ Hebbian- linked SOMs network when assessed on two bimodal datasets, an audio-acoustic speech utterance dataset and a phonological-semantics child utterance dataset. Of the three network architectures, the modified counterpropagation network achieves the highest percentage of correct classifications comparable to that of the LVQ-2 algorithm by Kohonen and the neural network for category learning by de Sa and Ballard in classification tasks using the audio-acoustic speech utterance dataset. To facilitate multimodal processing of temporal data, we propose a Temporal Hypermap neural network architecture that learns and recalls multiple temporal patterns in an unsupervised manner. The Temporal Hypermap introduces flexibility in the recall of temporal patterns - a stored temporal pattern can be retrieved by prompting the network with the temporal pattern's identity vector, whilst the incorporation of short term memory allows the recall of a temporal pattern, starting from the pattern item specified by contextual information up to the last item in the pattern sequence. Finally, we extend the connectionist modelling of child language acquisition in two important respects. First, we introduce the concept of multimodal representation of speech utterances at the one-word and two-word stage. This allows us to model child language at the one-word utterance stage with a single modified counterpropagation network, which is an improvement on previous models in which multiple networks are required to simulate the different aspects of speech at the one-word utterance stage. Secondly, we present, for the time, a connectionist model of the transition of child language from the one-word utterance stage to the two-word utterance stage. We achieve this using a gated multi-net comprising a modified counterpropagation network and a Temporal Hypermap.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available