Probabilistic machine learning and artificial intelligence.

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.The author acknowledges an EPSRC grant EP/I036575/1, the DARPA PPAML programme, a Google Focused Research Award for the Automatic Statistician and support from Microsoft Research.This is the author accepted manuscript. The final version is available from NPG at http://www.nature.com/nature/journal/v521/n7553/full/nature14541.html#abstract

Energy-based models for sparse overcomplete representations

We present a new way of extending independent components analysis (ICA) to overcomplete representations. In contrast to the causal generative extensions of ICA which maintain marginal independence of sources, we define features as deterministic (linear) functions of the inputs. This assumption results in marginal dependencies among the features, but conditional independence of the features given the inputs. By assigning energies to the features a probability distribution over the input states is defined through the Boltzmann distribution. Free parameters of this model are trained using the contrastive divergence objective (Hinton, 2002). When the number of features is equal to the number of input dimensions this energy-based model reduces to noiseless ICA and we show experimentally that the proposed learning algorithm is able to perform blind source separation on speech data. In additional experiments we train overcomplete energy-based models to extract features from various standard data-sets containing speech, natural images, hand-written digits and faces

Porous high-density polyethylene in facial reconstruction and revision rhinoplasty: a prospective cohort study

<p>Abstract</p> <p>Introduction</p> <p>Initial methods which used human tissues as reconstruction materials caused different problems including rejection, limited shapes and infection. In 1970s, PHDPE (Medpor®) was introduced by its exclusive advantageous including no donor site morbidity, easily shaped and the minimal foreign body reaction. Hereby, we report our experience of using Medpor® in facial reconstruction especially in frontal reconstruction and orbital rim with a large sample size.</p> <p>Methods</p> <p>This study was a prospective cohort study. Surgical techniques included using Medpor® in reconstruction of lamina papiracea (LP) (15 patients), frontal bone (15 patients), orbital rim (18 patients) and open rhinoplasty (8 patients). All interventions on LP were performed by endoscopic procedures. All frontal operations were carried out by bicoronal incision. In orbital defects, we used subciliary incision.</p> <p>Results</p> <p>From all 56 patients, 1 case had primitive neuroectodermal tumor (PNET) of maxillary sinus. In that case, reconstruction of inferior orbital rim was not successful and extrusion was occurred after radiotherapy. In rhinoplasty and other experiences no extrusion or infection were detected within the next 1 to 3 years of follow up. There were not any palpable and visible irregularities under the skin in our experiences.</p> <p>Conclusions</p> <p>In this study the patients did not experience any complications during the follow up periods and the satisfaction was remarkable. Gathering these data gives rise to future review studies which can provide more organized evidences for replacing classic reconstructive methods by the presented material.</p