The great progress achieved by communications in the last twenty years can be attested by the amount of audio-visual multimedia services available nowadays, such as digital television and IP-based video transmission. The success of these kind of services relies on their trustworthiness and the delivered quality of experience. Therefore, the development of efficient real-time quality monitoring tools that can quantify the audio-visual experience (as perceived by the end user) is key to the success of any multimedia service or application.
Adding a new sensing dimension to soft electronics: from the skin to below the skin
Abstract: Approximate probabilistic inference is a key computational task in modern machine learning, which allows us to reason with complex, structured, hierarchical (deep) probabilistic models to extract information and quantify uncertainty.
Abstract: We present a new statistical framework to quantify uncertainty (UQ) for recovering low-rank matrices from incomplete and noisy observations. We further develop a sequential active sampling approach guided by the uncertainties. The motivation comes from two related and widely studied problems, matrix completion, which aims to recover a low-rank matrix X from a partial, noisy observation of its entries, and low-rank matrix recovery, which recovers X from a set of linear combination its entries with additive noise.
Recent years have seen a flurry of activities in designing provably efficient nonconvex procedures for solving statistical estimation problems. The premise is that despite nonconvexity, the loss function may possess benign geometric properties that enable fast global convergence under carefully designed initializations, such as local strong convexity, local restricted convexity, etc.
*PLEASE NOTE CORRECTION: Seminar will take place in EER 3.646 (North Tower)