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Future technology: machine learning using memristors networks

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If you have a question about this talk, please contact Christian Steinruecken.

I discuss the properties of general networks made of a class of memristors (resistors with memory) which are used for non-conventional computing. In fact, these components emulate the plasticity of neurons and can be fabricated in specialized laboratories. After having extensively introduced these components, we discuss a differential equation which describes the evolution of the internal memory of a generic circuit for ideal memristors. This enables a formal treatment of the learning capability of these circuits. I then discuss the implications of such an equation for the use of memristors in machine learning, showing that in a certain limit of the parameter space the dynamics can be interpreted as a constrained gradient descent. I will also give a brief account of the formal connection to Statistical Mechanics at the end.

This talk is part of the Machine Learning @ CUED series.

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