196 citations · 537 across the 39 of their papers we have counts for
4 papers · 1 filter
Constructing Gradient Controllable Recurrent Neural Networks Using Hamiltonian Dynamics
Konstantin Rusch, John W. Pearson, Konstantinos C. Zygalakis
Recurrent neural networks (RNNs) have gained a great deal of attention in solving sequential learning problems. The learning of long-term dependencies, however, remains challenging…
PDE-Inspired Algorithms for Semi-Supervised Learning on Point Clouds
Oliver M. Crook, Tim Hurst, Carola-Bibiane Schönlieb +2
Given a data set and a subset of labels the problem of semi-supervised learning on point clouds is to extend the labels to the entire data set. In this paper we extend the labels b…
Contractivity of Runge-Kutta methods for convex gradient systems
J. M. Sanz-Serna, Konstantinos C. Zygalakis
We consider the application of Runge-Kutta (RK) methods to gradient systems , where, as in many optimization problems, is convex and (globall…
Accelerating proximal Markov chain Monte Carlo by using an explicit stabilised method
Luis Vargas, Marcelo Pereyra, Konstantinos C. Zygalakis
We present a highly efficient proximal Markov chain Monte Carlo methodology to perform Bayesian computation in imaging problems. Similarly to previous proximal Monte Carlo approach…