Doctoral colloquium - Matej Fandl (13.3.2023)

Monday 13.3.2023 at 13:10, Lecture room I/9

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Matej Fandl:
Attractor models of associative memory - on learning in modern Hopfield networks

Abstract:
Neural networks exhibiting point attractor dynamics are useful for modeling associative memory. A well known example is the Hopfield network, the modern variants of which got into the spotlight recently due to their huge storage capacity and usability in deep learning architectures. Our work builds on the interpretation of these networks as networks with 1 hidden layer of feature detectors. We see space for improvement in terms of training modern Hopfield networks, since the currently known methods either sacrifice training time for the computational complexity of the model, or the other way around. Our talk will describe our attempts at designing a novel learning rule for these networks, the use of which is expected to lead to fast and efficient distribution of labor between the hidden units.

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