95 citations · 160 across the 14 of their papers we have counts for
7 papers · 1 filter
Learning population and subject-specific brain connectivity networks via Mixed Neighborhood Selection
Ricardo Pio Monti, Christoforos Anagnostopoulos, Giovanni Montana
In neuroimaging data analysis, Gaussian graphical models are often used to model statistical dependencies across spatially remote brain regions known as functional connectivity. Ty…
Stopping criteria for boosting automatic experimental design using real-time fMRI with Bayesian optimization
Romy Lorenz, Ricardo P Monti, Ines R Violante +4
Bayesian optimization has been proposed as a practical and efficient tool through which to tune parameters in many difficult settings. Recently, such techniques have been combined…
Streaming regularization parameter selection via stochastic gradient descent
Ricardo Pio Monti, Romy Lorenz, Robert Leech +2
We propose a framework to perform streaming covariance selection. Our approach employs regularization constraints where a time-varying sparsity parameter is iteratively estimated v…
Graph embeddings of dynamic functional connectivity reveal discriminative patterns of task engagement in HCP data
Ricardo Pio Monti, Romy Lorenz, Peter Hellyer +3
There is increasing evidence to suggest functional connectivity networks are non-stationary. This has lead to the development of novel methodologies with which to accurately estima…
The Automatic Neuroscientist: automated experimental design with real-time fMRI
Romy Lorenz, Ricardo Pio Monti, Ines R. Violante +4
A standard approach in functional neuroimaging explores how a particular cognitive task activates a set of brain regions (one task-to-many regions mapping). Importantly though, the…
Measuring the functional connectome "on-the-fly": towards a new control signal for fMRI-based brain-computer interfaces
Ricardo Pio Monti, Romy Lorenz, Christoforos Anagnostopoulos +2
There has been an explosion of interest in functional Magnetic Resonance Imaging (MRI) during the past two decades. Naturally, this has been accompanied by many major advances in t…
Estimating Optimal Active Learning via Model Retraining Improvement
Lewis P. G. Evans, Niall M. Adams, Christoforos Anagnostopoulos
A central question for active learning (AL) is: "what is the optimal selection?" Defining optimality by classifier loss produces a new characterisation of optimal AL behaviour, by…