Recently, particle filters have attracted attentions for nonlinear state estimation. In this approaches, a posterior probability distribution of the state variable is evaluated based on observations in simulation using so-called importance sampling. We proposed a new filter, Evolution Strategies based particle (ESP) filter to circumvent degeneracy phenomena in the importance weights, which dete...

We introduce a compact coding of image information which explicitly separates visual information into geometric information (orientation) and structural information (phase and colour) and temporal information (optic flow). We investigate the importance of these visual attributes for stereo matching on a large data set. From these investigation we can conclude that it is the combination of diffe...

In the low observation noise limit particle filters become inefficient. In this paper a simple-to-implement particle filter is suggested as a solution to this well-known problem. The proposed Local Importance Sampling based particle filters draw the particles’ positions in a two-step process that makes use of both the dynamics of the system and the most recent observation. Experiments with the ...

We explore the use of autoregressive flows, a type generative model with tractable likelihood, as means efficient generation physical particle collider events. The usual maximum likelihood loss function is supplemented by an event weight, allowing for inference from samples variable, and even negative weights. To illustrate efficacy model, we perform experiments leading-order top pair productio...

We present an optimization approach for linear SVMs based on a stochasticprimal-dual approach, where the primal step is akin to an importance-weightedSGD, and the dual step is a stochastic update on the importance weights. Thisyields an optimization method with a sublinear dependence on the training setsize, and the first method for learning linear SVMs with runtime less the...

In decision-making models, the compositions (Aitchison, 1986) are employed in various forms. They can represent the normalized weights of criteria in multiple-criteria decision-making models, or the probabilities of states of the world in the models of decision making under risk. The normalized weights express the relative information about the importance of criteria, while the probabilities re...

Graph pattern mining aims at identifying structures that appear frequently in large graphs, under the assumption that frequency signi�es importance. Several measures of frequency have been proposed that respect the apriori property, essential for an e�cient search of the patterns. This property states that the number of appearances of a pattern in a graph cannot be larger than the frequency of ...