In many structured prediction problems, the highest-scoring labeling is hard to compute exactly, leading to the use of approximate inference methods. However, when inference is used in a learning algorithm, a good approximation of the score may not be sufficient. We show in particular that learning can fail even with an approximate inference method with rigorous approximation guarantees. There ...

In this paper, we present a novel method for solving structured prediction problems, based on combining Input Output Kernel Regression (IOKR) with an extension of magnitudepreserving ranking to structured output spaces. In particular, we concentrate on the case where a set of candidate outputs has been given, and the associated pre-image problem calls for ranking the set of candidate outputs. O...

Kernel machines such as kernel SVM and kernel ridge regression usually construct high quality models; however, their use in real-world applications remains limited due to the high prediction cost. In this paper, we present two novel insights for improving the prediction efficiency of kernel machines. First, we show that by adding “pseudo landmark points” to the classical Nyström kernel approxim...

This paper proposes a general matrix iterative model to represent a range of dynamic load balancing for MIMD parallel ray tracing. Diierent measure parameters are expressed only on theoretical data. A parallel ray tracing application is implemented. Both results obtained from the model and from parallel implementation are used to compare diierent load balancing strategies and validate the model...

Probabilistic weather forecasting consists of finding a joint probability distribution for future weather quantities or events. It is typically done by using a numerical weather prediction model, perturbing the inputs to the model in various ways, often depending on data assimilation, and running the model for each perturbed set of inputs. The result is then viewed as an ensemble of forecasts, ...

Let us declare that the most important devoir of any physical theory is to draw firm predictions for the outcome of laboratory experiments and astronomical observations. Unfortunately, the devoir is quite difficult to fulfill in the case of general relativity, essentially because of the complexity of the Einstein field equations, to which only few exact solutions are known. For instance, it is ...

We address the question of selecting a proper training set for neural network time series prediction or function approximation. As a result of analyzing the relation between approximation and generalization, a new measure, the generalization factor, is introduced. Using this factor and cross validation we develop the dynamic pattern selection algorithm. By employing two time series prediction t...