Archive

Web archives already hold together more than 534 billion files and this number continues to grow as new initiatives arise. Searching on all versions of these files acquired throughout time is challenging, since users expect as fast and precise answers from web archives as the ones provided by current web search engines. This talk discusses how to improve the search effectiveness of web archives through the...

Interest in Multi-Layer Perceptrons (MLPs) has spiked in recent years due to their central role in deep learning technologies. MLPs can be seen as a trainable multi-input multi-output function constructed by composing linear and non-linear functions organized into layers of nodes. A lesser known fact is that sigmoid-based MLPs also admit a probabilistic interpretation. Under this interpretation, the MLP forward-pass can be seen as approximating...

Depth sensing technology of existing RGB-D sensors (e.g. Kinect), is now capable of capturing reliable 3D information of our world in real-time. So far, this availability of Depth along with RGB Information has led several researchers to prove the usefulness of this type of multimodality on several computer vision tasks: Object recognition, categorization, detection and pose estimation. This talk will focus on the problem of object...

In image denoising and reconstruction, it is natural that the algebraic and statistical model of the observation be taken into account to formulate the optimization problems. There has been a lot of recent literature devoted to the respective denoising, deconvolution, and reconstruction problems, focusing on specific modalities such as ultrasound, SAR, fluorescence microscopy, MRI, etc. Our work aims at developing a framework unifying the denoising and...

This talk addresses the problem of parameter identification for a class of hybrid systems with continuous states and discrete time-varying parameters that can take different values from a finite set at each time instance. The identification of such systems typically results in non-convex formulation. Although these problems can be solved as a mixed integer program, the resulting complexity may be intractable. Another approach involves heuristics...

We present distributed optimization algorithms for minimizing the sum of convex functions, each one being the local cost function of an agent in a connected network. This finds applications in distributed learning, consensus, spectrum sensing for cognitive radio networks, resource allocation, etc. We propose fast gradient based approaches exhibiting less communication steps than currently available distributed algorithms for the same problem class and solution accuracy. The...

Much of modern data processing requires identification of low-dimensional structures in high-dimensional spaces, using observations that are incomplete or noisy. This general paradigm applies to the restoration of images (where natural images form a low-dimensional subset of the space of all possible images), compressed sensing (where the signal can be represented in terms of just a few elements of an appropriate basis), regularized regression (where...

Much of modern data processing requires identification of low-dimensional structures in high-dimensional spaces, using observations that are incomplete or noisy. This general paradigm applies to the restoration of images (where natural images form a low-dimensional subset of the space of all possible images), compressed sensing (where the signal can be represented in terms of just a few elements of an appropriate basis), regularized regression (where...

Convex optimisation and stochastic sampling are two powerful methodologies for performing statistical inference in inverse problems related to signal and image processing. It is widely acknowledged that these methodologies can complement each other very well; yet they are generally studied and used separately. In this talk I will discuss the potential for synergy between them and show some examples of how they can be combined...

Jet engines rank amongst the most complex machines ever built and are governed by deterministic and stochastic phenomena. Since jet engines are subject to extremely demanding operating conditions, a proper maintenance is critical to ensure high safety, maximum availability and minimum lifecycle costs. The deterioration of a turbomachine is a prime example of a complex stochastic process that is particularly challenging to model. During its...