Capsule Routing via Variational Bayes

Capsule networks are a recently proposed type of neural network shown to outperform alternatives in challenging shape recognition tasks. In capsule networks, scalar neurons are replaced with capsule vectors or matrices, whose entries represent different properties of objects. The relationships between objects and their parts are learned via trainable viewpoint-invariant transformation matrices, and the presence of a given object is decided by the level of agreement among votes from its parts. This interaction occurs between capsule layers and is a process called routing-by-agreement. In this paper, we propose a new capsule routing algorithm derived from Variational Bayes for fitting a mixture of transforming gaussians, and show it is possible transform our capsule network into a Capsule-VAE. Our Bayesian approach addresses some of the inherent weaknesses of MLE based models such as the variance-collapse by modelling uncertainty over capsule pose parameters. We outperform the state-of-the-art on smallNORB using 50% fewer capsules than previously reported, achieve competitive performances on CIFAR-10, Fashion-MNIST, SVHN, and demonstrate significant improvement in MNIST to affNIST generalisation over previous works

ProtoCaps : A Fast and Non-Iterative Capsule Network Routing Method

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Object-Centric Learning with Capsule Networks : A Survey

The authors would like to thank all reviewers, and especially Professor Chris Williams from the School of Informatics of the University of Edinburgh, who provided constructive feedback and ideas on how to improve this work.Peer reviewe

Novel Computational Approaches For Multidimensional Brain Image Analysis

The overall goal of this dissertation is focused on addressing challenging problems in 1D, 2D/3D and 4D neuroimaging by developing novel algorithms that combine signal processing and machine learning techniques. One of these challenging tasks is the accurate localization of the eloquent language cortex in brain resection pre-surgery patients. This is especially important since inaccurate localization can lead to diminshed functionalities and thus, a poor quality of life for the patient. The first part of this dissertation addresses this problem in the case of drug-resistant epileptic patients. We propose a novel machine learning based algorithm to establish an alternate electrical stimulation-free approach, electro-corticography (ECoG) as a viable technique for localization of the eloqeunt language cortex. We process the 1D signals in frequency domain to train a classifier and identify language responsive electrodes from the surface of the brain. We then enhance the proposed approach by developing novel multi-modal deep learning algorithms. We test different aspects of the experimental paradigm and identify the best features and models for classification. Another difficult neuroimaging task is that of identifying biomarkers of a disease. This is even more challenging considering that skill acquisition leads to neurological changes. We propose to help understand these changes in the brain of chess masters via a multi-modal approach that combines 3D and 4D imaging modalities in a novel way. The proposed approaches may help narrow the regions to be tested in pre-surgical localization tasks and in better surgery planning. The proposed work may also pave the way for a holistic view of the human brain by combining several modalities into one. Finally, we deal with the problem of learning strong signal representations/features by proposing a novel capsule based variational autoencoder, B-Caps. The proposed B-Caps helps in learning a strong feature representation that can be used with multi-dimensional data