Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the en...
We describe a method for recovering 3D human body pose from silhouettes. Our model is based on learning a latent space using the Gaussian Process Latent Variable Model (GP-LVM) [1]...
Carl Henrik Ek, Philip H. S. Torr, Neil D. Lawrenc...
Approximately 50% of all patients with intraocular melanoma die of metastatic disease, despite successful treatment of the primary tumour. The main factors associated with mortalit...
Azzam F. Taktak, Antonio Eleuteri, Christian Setzk...
Abstract. A challenge for virtual reality (VR) applications is to increase the realism of an observer’s visual experience. For this purpose the variation of the blur an observer ...
We present a probabilistic program-transformation algorithm to render a given program tamper-resistant. In addition, we suggest a model to estimate the required effort for an atta...
Nenad Dedic, Mariusz H. Jakubowski, Ramarathnam Ve...
Model transformations are a key element in the OMG’s Model Driven Development agenda. They did not begin here: the fundamental idea of transforming, automatically, one model into...
—Due to the dynamic nature of grid environments, schedule algorithms always need assistance of a long-time-ahead load prediction to make decisions on how to use grid resources ef...
Spatio-temporal network is defined by a set of nodes, and a set of edges, where the properties of nodes and edges may vary over time. Such networks are encountered in a variety of...
: There are several models for the evolutionary process forming a species tree. We examine the Birth-and-Death model (BDM), the Proportional-to-Distinguishable Arrangements (PDA) m...
Abstract. The simple intramolecular model for gene assembly in ciliates is particularly interesting because it can predict the correct assembly of all available experimental data, ...