3 citations · 5 across the 7 of their papers we have counts for
7 papers
Deep Feedback Models
David Calhas, Arlindo L. Oliveira
Deep Feedback Models (DFMs) are a new class of stateful neural networks that combine bottom up input with high level representations over time. This feedback mechanism introduces d…
Deep Recurrence for Dynamical Segmentation Models
David Calhas, Arlindo L. Oliveira
While biological vision systems rely heavily on feedback connections to iteratively refine perception, most artificial neural networks remain purely feedforward, processing input i…
The Role of Recurrency in Image Segmentation for Noisy and Limited Sample Settings
David Calhas, João Marques, Arlindo L. Oliveira
The biological brain has inspired multiple advances in machine learning. However, most state-of-the-art models in computer vision do not operate like the human brain, simply becaus…
EEG to fMRI Synthesis Benefits from Attentional Graphs of Electrode Relationships
David Calhas, Rui Henriques
Topographical structures represent connections between entities and provide a comprehensive design of complex systems. Currently these structures are used to discover correlates of…
EEG to fMRI Synthesis: Is Deep Learning a candidate?
David Calhas, Rui Henriques
Advances on signal, image and video generation underly major breakthroughs on generative medical imaging tasks, including Brain Image Synthesis. Still, the extent to which function…
fMRI Multiple Missing Values Imputation Regularized by a Recurrent Denoiser
David Calhas, Rui Henriques
Functional Magnetic Resonance Imaging (fMRI) is a neuroimaging technique with pivotal importance due to its scientific and clinical applications. As with any widely used imaging mo…