2 research outputs found
A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification
Recent applications of pattern recognition techniques on brain connectome
classification using functional connectivity (FC) neglect the non-Euclidean
topology and causal dynamics of brain connectivity across time. In this paper,
a deep probabilistic spatiotemporal framework developed based on variational
Bayes (DSVB) is proposed to learn time-varying topological structures in
dynamic brain FC networks for autism spectrum disorder (ASD) identification.
The proposed framework incorporates a spatial-aware recurrent neural network to
capture rich spatiotemporal patterns across dynamic FC networks, followed by a
fully-connected neural network to exploit these learned patterns for
subject-level classification. To overcome model overfitting on limited training
datasets, an adversarial training strategy is introduced to learn graph
embedding models that generalize well to unseen brain networks. Evaluation on
the ABIDE resting-state functional magnetic resonance imaging dataset shows
that our proposed framework significantly outperformed state-of-the-art methods
in identifying ASD. Dynamic FC analyses with DSVB learned embeddings reveal
apparent group difference between ASD and healthy controls in network profiles
and switching dynamics of brain states
Cross-domain Transfer Learning and State Inference for Soft Robots via a Semi-supervised Sequential Variational Bayes Framework
Recently, data-driven models such as deep neural networks have shown to be
promising tools for modelling and state inference in soft robots. However,
voluminous amounts of data are necessary for deep models to perform
effectively, which requires exhaustive and quality data collection,
particularly of state labels. Consequently, obtaining labelled state data for
soft robotic systems is challenged for various reasons, including difficulty in
the sensorization of soft robots and the inconvenience of collecting data in
unstructured environments. To address this challenge, in this paper, we propose
a semi-supervised sequential variational Bayes (DSVB) framework for transfer
learning and state inference in soft robots with missing state labels on
certain robot configurations. Considering that soft robots may exhibit distinct
dynamics under different robot configurations, a feature space transfer
strategy is also incorporated to promote the adaptation of latent features
across multiple configurations. Unlike existing transfer learning approaches,
our proposed DSVB employs a recurrent neural network to model the nonlinear
dynamics and temporal coherence in soft robot data. The proposed framework is
validated on multiple setup configurations of a pneumatic-based soft robot
finger. Experimental results on four transfer scenarios demonstrate that DSVB
performs effective transfer learning and accurate state inference amidst
missing state labels. The data and code are available at
https://github.com/shageenderan/DSVB.Comment: Accepted at the International Conference on Robotics and Automation
(ICRA) 202