Single-cell subcellular protein localisation using novel ensembles of diverse deep architectures.

Unravelling protein distributions within individual cells is vital to understanding their function and state and indispensable to developing new treatments. Here we present the Hybrid subCellular Protein Localiser (HCPL), which learns from weakly labelled data to robustly localise single-cell subcellular protein patterns. It comprises innovative DNN architectures exploiting wavelet filters and learnt parametric activations that successfully tackle drastic cell variability.

Understanding the Distributions of Aggregation Layers in Deep Neural Networks.

The process of aggregation is ubiquitous in almost all the deep nets' models. It functions as an important mechanism for consolidating deep features into a more compact representation while increasing the robustness to overfitting and providing spatial invariance in deep nets. In particular, the proximity of global aggregation layers to the output layers of DNNs means that aggregated features directly influence the performance of a deep net.

Robust Facial Feature Tracking Using Shape-Constrained Multiresolution-Selected Linear Predictors.

This paper proposes a learned data-driven approach for accurate, real-time tracking of facial features using only intensity information. The task of automatic facial feature tracking is nontrivial since the face is a highly deformable object with large textural variations and motion in certain regions. Existing works attempt to address these problems by either limiting themselves to tracking feature points with strong and unique visual cues (e.