Coronary X-ray angiography is a crucial clinical procedure for the diagnosis and treatment of coronary artery disease, which accounts for roughly 16% of global deaths every year. However, the images acquired in these procedures have low resolution and poor contrast, … Read More
S1 (2009-2010)
Network Inference from Co-occurences
Inferring network structures is a central problem arising in numerous fields of science and technology, including communication systems, biology, sociology, and neuroscience. Unfortunately, it is often difficult, or impossible, to obtain data that directly reveal the underlying network structure, and … Read More
An Iterative, Constrained Approach for Pitch Component Extraction
In this talk I will describe an approach for automatic extraction of global and local patterns of pitch(F0) contours taking into account the overall trends of these phenomena in the presented data. We propose an iterative algorithm to optimally extract … Read More
Learning simple texture discrimination filters
Everyday tasks like walking on the street, recognizing a friend or understanding a scene seem so simple and immediate that transposing it to a computer might seem like an easy task. Only when we try it do we realize our … Read More
Mind The Gap: Reconstruction of missing cardiovascular signals using adaptive filtering
In this talk I will introduce a robust method for filling in short missing segments in multiparameter Intensive Care Unit cardiovascular data. This work was inspired by the “PhysioNet/Computing in Cardiology Challenge 2010: Mind the Gap”. The interconnections between the … Read More
From elements to networks of neuronal activity – a machine learning approach
Neuroinformatics “combines neuroscience and the information sciences to develop and apply advanced tools for a major advancement in understanding the structure and function of the brain.” After introducing the speaker’s neuroinformatics research group, we will address issues related to the … Read More
Posterior Regularization Framework: Learning Tractable Models with Intractable Constraints
Unsupervised Learning of probabilistic structured models presents a fundamental tradeoff between richness of captured constraints and correlations versus efficiency and tractability of inference. In this thesis, we propose a new learning framework called Posterior Regularization that incorporates side-information into unsupervised … Read More
Decision-theoretic Planning under Uncertainty for Active Cooperative Perception
As robots leave research labs to operate more often in human-inhabited, larger environments, cooperation between sensor networks and mobile robots becomes crucial. For example, in urban scenarios, employing mobile robots is a need to augment the limited sensor coverage and … Read More
Gradient Approaches to Reinforcement Learning
In this talk I will present an overview of some of the past and current lines of research in reinforcement learning (RL), as well as some of the challenges that research in this area has faced in the last decades. … Read More
Multimodal pattern matching algorithms and applications
After introducing myself and where I come from, in this talk I will focus on 3 projects I have been working in the last year. The first one is a novel pattern matching algorithm, based on the well known Dynamic … Read More