11 citations · 18 across the 6 of their papers we have counts for
6 papers
Score-Aware Policy-Gradient and Performance Guarantees using Local Lyapunov Stability
Céline Comte, Matthieu Jonckheere, Jaron Sanders +1
In this paper, we introduce a policy-gradient method for model-based reinforcement learning (RL) that exploits a type of stationary distributions commonly obtained from Markov deci…
Detection and Evaluation of Clusters within Sequential Data
Alexander Van Werde, Albert Senen-Cerda, Gianluca Kosmella +1
Sequential data is ubiquitous -- it is routinely gathered to gain insights into complex processes such as behavioral, biological, or physical processes. Challengingly, such data no…
Spectral norm bounds for block Markov chain random matrices
Jaron Sanders, Albert Senen-Cerda
This paper quantifies the asymptotic order of the largest singular value of a centered random matrix built from the path of a Block Markov Chain (BMC). In a BMC there are label…
Universal Approximation in Dropout Neural Networks
Oxana A. Manita, Mark A. Peletier, Jacobus W. Portegies +2
We prove two universal approximation theorems for a range of dropout neural networks. These are feed-forward neural networks in which each edge is given a random -valued f…
Asymptotic convergence rate of Dropout on shallow linear neural networks
Albert Senen-Cerda, Jaron Sanders
We analyze the convergence rate of gradient flows on objective functions induced by Dropout and Dropconnect, when applying them to shallow linear Neural Networks (NNs) - which can…
Almost Sure Convergence of Dropout Algorithms for Neural Networks
Albert Senen-Cerda, Jaron Sanders
We investigate the convergence and convergence rate of stochastic training algorithms for Neural Networks (NNs) that have been inspired by Dropout (Hinton et al., 2012). With the g…