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- W2240555334 abstract "This talk will explore the integration of learning modes into a single neural network structure in which layers of neurons and even individual neurons adopt different modes. There are several reasons to explore modal learning in neural networks. One motivation is to overcome the inherent limitations of any given mode (for example some modes memorise specific features, others average across features, and both approaches may be relevant according to the circumstances); another is inspiration from neuroscience, cognitive science and human learning, where it is impossible to build a serious model without consideration of multiple modes; and a third reason is non-stationary input data, or time-variant learning objectives, where the required mode is a funtion of time. Several modal learning ideas will be presented: The Snap-Drift Neural Network (SDNN) which toggles its learning between two modes, either unsupervised or guided by performance feedback (reinforcement); a general approach to swapping between several learning modes in real-time; and an adaptive function neural network (ADFUNN), in which adaptation applies simultaneously to both the weights and the individual neuron activation functions. Examples will be drawn from a range of applications such as natural language parsing, speech processing, geographical location systems, optical character recognition and virtual learning environments." @default.
- W2240555334 created "2016-06-24" @default.
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- W2240555334 date "2008-05-02" @default.
- W2240555334 modified "2023-09-24" @default.
- W2240555334 title "Plenary lecture II: meta-adaptation: neurons that change their mode" @default.
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