This paper presents our Recurrent Control Neural Network (RCNN), which is a model-based approach for a data-efficient modelling and control of reinforcement learning problems in di...
Compressive Sensing (CS) combines sampling and compression into a single subNyquist linear measurement process for sparse and compressible signals. In this paper, we extend the th...
Volkan Cevher, Marco F. Duarte, Chinmay Hegde, Ric...
Gaussian Markov random fields (GMRFs) are useful in a broad range of applications. In this paper we tackle the problem of learning a sparse GMRF in a high-dimensional space. Our a...
We present a new semi-supervised training procedure for conditional random fields (CRFs) that can be used to train sequence segmentors and labelers from a combination of labeled a...
Feng Jiao, Shaojun Wang, Chi-Hoon Lee, Russell Gre...
BioAmbients is a derivative of mobile ambients that has shown promise of describing interesting features of the behaviour of biological systems. The technical contribution of this...
Henrik Pilegaard, Hanne Riis Nielson, Flemming Nie...