Search Results for author:
Found 9 papers, 4 papers with code
Scaling up ridge regression for brain encoding in a massive individual fMRI dataset
This paper evaluates different parallelization techniques to reduce the training time of brain encoding with ridge regression on the CNeuroMod Friends dataset, one of the largest deep fMRI resource currently available.
Generative Adversarial Neuroevolution for Control Behaviour Imitation
There is a recent surge in interest for imitation learning, with large human video-game and robotic manipulation datasets being used to train agents on very complex tasks.
Neuroevolution of Recurrent Architectures on Control Tasks
Simple evolutionary algorithms have recently been shown to also be capable of optimizing deep neural network parameters, at times matching the performance of gradient-based techniques, e. g. in reinforcement learning settings.
First order expansion of convex regularized estimators
Such first order expansion implies that the risk of $\hat{\beta}$ is asymptotically the same as the risk of $\eta$ which leads to a precise characterization of the MSE of $\hbeta$; this characterization takes a particularly simple form for isotropic design.
Modelling Working Memory using Deep Recurrent Reinforcement Learning
We train these neural networks to solve the working memory task by training them with a sequence of images in supervised and reinforcement learning settings.
Functional Annotation of Human Cognitive States using Graph Convolution Networks
In this project, we applied graph convolutional networks (GCN) to decode brain activity over short time windows in a task fMRI dataset, i. e. associate a given window of fMRI time series with the task used.
A brain signature highly predictive of future progression to Alzheimer's dementia
Early prognosis of Alzheimer's dementia is hard.
Statistical power and prediction accuracy in multisite resting-state fMRI connectivity
We then implemented a series of Monte-Carlo simulations, based on real data, to evaluate the impact of the multisite effects on detection power in statistical tests comparing two groups (with and without the effect) using a general linear model, as well as on the prediction of group labels with a support-vector machine.
A Bayesian alternative to mutual information for the hierarchical clustering of dependent random variables
We found that the Bayesian formulation naturally shrinks the empirical covariance matrix towards a matrix set a priori (e. g., the identity), provides an automated stopping rule, and corrects for dimensionality using a term that scales up the measure as a function of the dimensionality of the variables.
