6 citations · 10 across the 7 of their papers we have counts for
7 papers
Frequency selection for the diagnostic characterization of human brain tumours
Carlos Arizmendi, Alfredo Vellido, Enrique Romero
The diagnosis of brain tumours is an extremely sensitive and complex clinical task that must rely upon information gathered through non-invasive techniques. One such technique is m…
Mean field initialization of the Annealed Importance Sampling algorithm for an efficient evaluation of the Partition Function of Restricted Boltzmann Machines
A. Prat Pou, E. Romero, J. Martí +1
Probabilistic models in physics often require from the evaluation of normalized Boltzmann factors, which in turn implies the computation of the partition function Z. Getting the ex…
Efficient Evaluation of the Partition Function of RBMs with Annealed Importance Sampling
Ferran Mazzanti, Enrique Romero
Probabilistic models based on Restricted Boltzmann Machines (RBMs) imply the evaluation of normalized Boltzmann factors, which in turn require from the evaluation of the partition…
On the use of Pairwise Distance Learning for Brain Signal Classification with Limited Observations
David Calhas, Enrique Romero, Rui Henriques
The increasing access to brain signal data using electroencephalography creates new opportunities to study electrophysiological brain activity and perform ambulatory diagnoses of n…
Weighted Contrastive Divergence
Enrique Romero Merino, Ferran Mazzanti Castrillejo, Jordi Delgado Pin +1
Learning algorithms for energy based Boltzmann architectures that rely on gradient descent are in general computationally prohibitive, typically due to the exponential number of te…
A Neighbourhood-Based Stopping Criterion for Contrastive Divergence Learning
E. Romero, F. Mazzanti, J. Delgado
Restricted Boltzmann Machines (RBMs) are general unsupervised learning devices to ascertain generative models of data distributions. RBMs are often trained using the Contrastive Di…
Stopping Criteria in Contrastive Divergence: Alternatives to the Reconstruction Error
David Buchaca, Enrique Romero, Ferran Mazzanti +1
Restricted Boltzmann Machines (RBMs) are general unsupervised learning devices to ascertain generative models of data distributions. RBMs are often trained using the Contrastive Di…