6,285 research outputs found
Adversarial Variational Embedding for Robust Semi-supervised Learning
Semi-supervised learning is sought for leveraging the unlabelled data when
labelled data is difficult or expensive to acquire. Deep generative models
(e.g., Variational Autoencoder (VAE)) and semisupervised Generative Adversarial
Networks (GANs) have recently shown promising performance in semi-supervised
classification for the excellent discriminative representing ability. However,
the latent code learned by the traditional VAE is not exclusive (repeatable)
for a specific input sample, which prevents it from excellent classification
performance. In particular, the learned latent representation depends on a
non-exclusive component which is stochastically sampled from the prior
distribution. Moreover, the semi-supervised GAN models generate data from
pre-defined distribution (e.g., Gaussian noises) which is independent of the
input data distribution and may obstruct the convergence and is difficult to
control the distribution of the generated data. To address the aforementioned
issues, we propose a novel Adversarial Variational Embedding (AVAE) framework
for robust and effective semi-supervised learning to leverage both the
advantage of GAN as a high quality generative model and VAE as a posterior
distribution learner. The proposed approach first produces an exclusive latent
code by the model which we call VAE++, and meanwhile, provides a meaningful
prior distribution for the generator of GAN. The proposed approach is evaluated
over four different real-world applications and we show that our method
outperforms the state-of-the-art models, which confirms that the combination of
VAE++ and GAN can provide significant improvements in semisupervised
classification.Comment: 9 pages, Accepted by Research Track in KDD 201
Detection of Pathological HFO Using Supervised Machine Learning and iEEG Data
Epilepsy is the second most common neurological disorder and it affects approxi mately 50 million people worldwide. One of the main characteristics of this disorder is the presence of recurrent seizures which tend to be controlled through medication. Nonetheless, 20% of the patients with this disorder are resistant to drug treatment meaning that they need to go through alternative procedures
MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals
Brain signals are important quantitative data for understanding physiological
activities and diseases of human brain. Most existing studies pay attention to
supervised learning methods, which, however, require high-cost clinical labels.
In addition, the huge difference in the clinical patterns of brain signals
measured by invasive (e.g., SEEG) and non-invasive (e.g., EEG) methods leads to
the lack of a unified method. To handle the above issues, we propose to study
the self-supervised learning (SSL) framework for brain signals that can be
applied to pre-train either SEEG or EEG data. Intuitively, brain signals,
generated by the firing of neurons, are transmitted among different connecting
structures in human brain. Inspired by this, we propose MBrain to learn
implicit spatial and temporal correlations between different channels (i.e.,
contacts of the electrode, corresponding to different brain areas) as the
cornerstone for uniformly modeling different types of brain signals.
Specifically, we represent the spatial correlation by a graph structure, which
is built with proposed multi-channel CPC. We theoretically prove that
optimizing the goal of multi-channel CPC can lead to a better predictive
representation and apply the instantaneou-time-shift prediction task based on
it. Then we capture the temporal correlation by designing the
delayed-time-shift prediction task. Finally, replace-discriminative-learning
task is proposed to preserve the characteristics of each channel. Extensive
experiments of seizure detection on both EEG and SEEG large-scale real-world
datasets demonstrate that our model outperforms several state-of-the-art time
series SSL and unsupervised models, and has the ability to be deployed to
clinical practice
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