In the talk I will discuss work on learning to rank for information retrieval, in which the goal is to automatically construct a model that ranks documents in response to a query. In traditional supervised machine learning approaches for the LTR problem one manually selects a set of manually engineered ranking features and then learns the best way of combining them to obtain the most powerful...
In every sector of human activity, the pervasiveness of sensors and the accumulation of digital information have raised novel intellectual challenges, dreams and fears. Recently, intensive research in the field of high dimensional statistics, the progress in the description and modeling of networks, and the second life of optimization theory have generated concepts and algorithms that allow to...
Juan Pablo Bello(New York University / Telecom ParisTech)
30/06/2014 15:05
This talk discusses a mix of concepts, problems and techniques at the crossroads of signal processing, machine learning and music. I will start by introducing content-based music information retrieval (MIR) as an important and challenging data science problem. Then, I will discuss recent work done at my lab on a variety of MIR problems such as automatic chord recognition, music structure...
Since the size and complexity of scientific datasets is growing at a very high rate, people are working on developing techniques to effectively depict and visualize them. However, frequently it is not sufficient to just produce a single static visualization but instead we have to support scientists in discovering aspects about the data that they did not know about it. That means that we have...
