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DTSTART:20181028T020000
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DTSTAMP:20260403T220401Z
UID:5c265de4c2cce904949525@ist.ac.at
DTSTART:20190128T100000
DTEND:20190128T110000
DESCRIPTION:Speaker: Laurence Aitchison\nhosted by Peter Jonas\nAbstract: H
 ere\, I present three projects using principled\, Bayesian treatments of u
 ncertainty to address pressing problems in modern deep learning.1.) Bayesi
 an inference in modern convolutional neural networks.Bayesian inference ov
 er the parameters of a neural networks is critical to improve generalisati
 on performance\, and to reason about what the network does not know due to
  limited training data. However\, Bayesian inference in typical neural net
 works is impossible (at least without severe approximations) due to the sh
 eer number of parameters in these networks. Here\, we show exact inference
  is possible in state-of-the-art convolutional networks\, if we take the l
 imit of infinitely many convolutional filters (at which point the outputs 
 follow a Gaussian process). The network obtains 0.84 % classification erro
 r on MNIST\, a new record for a Gaussian Process method.2.) Neural network
  optimization as Bayesian inferenceNeural network optimization methods fal
 l into two broad classes: adaptive methods such as RMSprop and non-adaptiv
 e methods such as stochastic gradient descent (SGD). This presents a probl
 em for practitioners: which method should use on a particular problem? Or 
 even should you use an adaptive method on some parameters\, and a non-adap
 tive method on others? Here\, we resolve this issue\, by deriving a Bayesi
 an gradient descent rule that adaptively transitions between adaptive and 
 non-adaptive behaviour. This method provides insight into when we might ex
 pect adaptive and non-adaptive methods to be most useful\, and is superior
  to standard neural network optimization methods in practice.3.) Bayesian 
 inference in deep graphical models Graphical models are a powerful languag
 e to encode our knowledge of the dependency (or even causal) structure in 
 data. However\, graphical modelling has fallen out of favour recently\, du
 e to the success of often unstructured deep-learning. Here\, we show that 
 it is possible to combine deep learning and graphical models to form "deep
  graphical models". To perform inference\, we combine strategies from deep
  learning (variational autoencoder recognition models) with strategies fro
 m graphical modelling (message passing). The resulting inference schemes g
 ive considerably improved performance over a vanilla deep-learning inferen
 ce strategy.
LOCATION:Mondi Seminar Room 2\, Central Building\, ISTA
ORGANIZER:tguggenb@ist.ac.at
SUMMARY:Laurence Aitchison: Bayesian inference and deep learning
URL:https://talks-calendar.ista.ac.at/events/1719
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