Memories of Artificial Neural Networks
Bjorn Schuller
(TU Munich, Germany)
Abstract:
Artificial Neural Networks have recently undergone a certain renaissance given
their manifold strengths and recent developments. They usually suffer
from a lack of or vanishing memory - even when being recurrent. This
comes, as the memory is typically not modelled explicitly. The long short
term memory (LSTM) paradigm helps overcome this gap. Likewise, a network
can actually "memorize", but it can also learn when to forget. This
allows analysing the time span that has been learnt as being of merit.
In this vein, this talk deals with analysing "memories" of LSTM networks
focussing on audio/visual processing tasks including best results in
several recent research challenges. It further shows how to combine LSTM
with dynamic Bayesian networks to reach dynamic abilities along-side
explicit memory modelling.
Bio:
Björn Schuller received his diploma, doctoral degree, and habilitation in
electrical engineering and information technology from TU Munich/Germany.
He is a full professor heading the Institute for Sensor Systems at the
University of Passau/Germany and the Machine Intelligence and Signal
Processing (MISP) Group at TUM, and is the CEO of audEERING. Further, he
is a permanent visiting professor at the Harbin Institute of
Technology/P.R. China, and associate researcher of the University of
Geneva/Switzerland and JOANNEUM RESEARCH in Graz/Austria. Previously, he
was a guest lecturer at UNIVPM in Ancona/Italy, with the CNRS-LIMSI in
Orsay/France, and visiting scientist of the Imperial College London/UK
and NICTA in Sydney/Australia. Dr. Schuller (co-)authored 5 books and
more than 300 publications (>4,000 citations, h-index = 33). He was
co-founding steering committee member and guest editor, and still serves
as associate editor of the IEEE Transactions on Affective Computing, the
IEEE Transactions on Neural Networks and Learning Systems, the IEEE
Transactions on Cybernetics, and Computer Speech and Language, and as
guest editor for the IEEE Intelligent Systems Magazine, Neural Networks,
Speech Communication, Image and Vision Computing, Cognitive Computation,
and the EURASIP Journal on Advances in Signal Processing.