AIMC Topic: Unsupervised Machine Learning

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High-Fidelity Monocular Face Reconstruction Based on an Unsupervised Model-Based Face Autoencoder.

IEEE transactions on pattern analysis and machine intelligence
In this work, we propose a novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image. To this end, we combine a convolutional encoder network...

Unsupervised and real-time spike sorting chip for neural signal processing in hippocampal prosthesis.

Journal of neuroscience methods
BACKGROUND: Damage to the hippocampus will result in the loss of ability to form new long-term memories and cognitive disorders. At present, there is no effective medical treatment for this issue. Hippocampal cognitive prosthesis is proposed to repla...

Interactive reservoir computing for chunking information streams.

PLoS computational biology
Chunking is the process by which frequently repeated segments of temporal inputs are concatenated into single units that are easy to process. Such a process is fundamental to time-series analysis in biological and artificial information processing sy...

Laplacian mixture modeling for network analysis and unsupervised learning on graphs.

PloS one
Laplacian mixture models identify overlapping regions of influence in unlabeled graph and network data in a scalable and computationally efficient way, yielding useful low-dimensional representations. By combining Laplacian eigenspace and finite mixt...

Bio-inspired visual self-localization in real world scenarios using Slow Feature Analysis.

PloS one
We present a biologically motivated model for visual self-localization which extracts a spatial representation of the environment directly from high dimensional image data by employing a single unsupervised learning rule. The resulting representation...

Application of identity vectors for EEG classification.

Journal of neuroscience methods
BACKGROUND: Finding an optimal EEG subject verification algorithm is a long standing goal within the EEG community. For every advancement made, another feature set, classifier, or dataset is often introduced; tracking improvements in classification w...

MRI-based automated detection of implanted low dose rate (LDR) brachytherapy seeds using quantitative susceptibility mapping (QSM) and unsupervised machine learning (ML).

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Permanent seed brachytherapy is an established treatment option for localized prostate cancer. Currently, post-implant dosimetry is performed on CT images despite challenging target delineation due to limited soft tissue contr...

Synthesizing and Reconstructing Missing Sensory Modalities in Behavioral Context Recognition.

Sensors (Basel, Switzerland)
Detection of human activities along with the associated context is of key importance for various application areas, including assisted living and well-being. To predict a user's context in the daily-life situation a system needs to learn from multimo...

Regularized aggregation of statistical parametric maps.

Human brain mapping
Combining statistical parametric maps (SPM) from individual subjects is the goal in some types of group-level analyses of functional magnetic resonance imaging data. Brain maps are usually combined using a simple average across subjects, making them ...