We consider the problem of sensor selection for event detection in wireless sensor networks (WSNs). We want to choose a subset of p out of n sensors …

In image analysis, many tasks require representing two-dimensional (2D) shape, often specified by a set of 2D points, for comparison purposes. The ch…

Estimation of rigid body attitude motion is a long-standing problem of interest in several applications. This problem is challenging primarily becaus…

In this paper, we develop deep spatio-temporal neural networks to sequentially count vehicles from low quality videos captured by city cameras (cityc…

Aiming to reduce pollutant emissions, bicycles are regaining popularity specially in urban areas. However, the number of cyclists' fatalities is not …

We propose AD3, a new algorithm for approximate maximum a posteriori (MAP) inference on factor graphs based on the alternating directions method of m…

We establish the large deviations asymptotic performance (error exponent) of consensus+innovations distributed detection over random networks with ge…

We study the large deviations performance, i.e., the exponential decay rate of the error probability, of distributed detection algorithms over random…

The paper studies the highly prototypical Fictitious Play (FP) algorithm, as well as a broad class of learning processes based on best-response dynam…

We study distributed optimization where nodes cooperatively minimize the sum of their individual, locally known, convex costs $f_i(x)$'s, $x \in {\ma…

Processing data collected by a network of agents often boils down to solving an optimization problem. The distributed nature of these problems calls …

We applied large deviations theory to analyze the performance of the running consensus distributed detection algorithm. We considered spatially correlated Gaussian noise and time varying networks. With running consensus, the state at each node is up…

We apply large deviations theory to study asymptotic performance of running consensus distributed detection in sensor networks. Running consensus is …

Decision-theoretic planning is a popular approach to sequential decision making problems, because it treats uncertainty in sensing and acting in a pr…

The majority of the approaches to the automatic recovery of a panoramic image from a set of partial views are suboptimal in the sense that the input …

Distributed consensus and other linear systems with system stochastic matrices $W_k$ emerge in various settings, like opinion formation in social net…

Despite the recent progress towards efficient multiple kernel learning (MKL), the structured output case remains an open research front. Current appr…

We study distributed optimization problems when $N$ nodes minimize the sum of their individual costs subject to a common vector variable. The costs a…

Consider a set of agents that wish to estimate a vector of parameters of their mutual interest. For this estimation goal, agents can sense and commun…

We designed a consensus+innovations distributed detector that achieves exponential decay rate of the detection error probability at all sensors under noisy communication links, and even when certain (or most sensors) in isolation cannot perform succ…

We study the large deviations performance of consensus+innovations distributed detection over noisy networks, where sensors at a time step k cooperat…

We address the problem of bootstrapping language acquisition for an artificial system similarly to what is observed in experiments with human infants…

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