S8 (2016-2017)

Going Neurotic With Neural Word Embeddings… again!

Word embeddings, such as Word2Vec or Glove, are vector representations that capture lexical-semantic properties of words. They constitute a practical way for transferring knowledge between two machine learning models, and they contribute to greatly reducing the learning time required for … Read More

Learning from distributed datasets

Modern datasets are increasingly collected by teams of agents that are spatially distributed: sensor networks, networks of cameras, and teams of robots. To extract information in a scalable manner from those distributed datasets, we need distributed learning. In the vision … Read More

Neuroscience: a control theory and network science perspective

One fundamental challenge of our time is to understand the neuroscience of brain and it’s relation to neurological disease as well as human behaviors. In this talk, I will argue that tools from control systems, dynamics, and network science can … Read More

Shape-Based Trajectory Clustering

Automatic trajectory classification has countless applications, ranging from the natural sciences, such as zoology and meteorology, to urban planning and sports analysis, and has generated great interest and investigation. The purpose of this work is to propose and test new … Read More

Distributed inference on networks: algorithms for agent self-localization

Operation of teams of artificial helpers is one of the hallmarks of technology for the near future, both in hazardous situations and in everyday life. In harsh environments as sea exploration and exploitation, search and rescue operations, and even in … Read More

Semi-Supervised Learning of Sequence Models with the Method of Moments

In this talk I will present work presented at EMNLP 2016, about a fast and scalable method for semi-supervised learning of sequence models. The proposed method is based on anchor words and moment matching techniques to retrieve the hidden assignment … Read More

Tackling Inverse Problems in Imaging Using Targeted Priors

Image denoising is one of the core problems in image processing and some argue that current state-of-the-art general purpose methods may be reaching the maximum possible performance. This talk focuses on two main topics: 1) how can we push such … Read More

Linguistic Benchmarks of Online News Article Quality

Online news editors ask themselves the same question many times: what is missing in this news article to go online? This is not an easy question to be answered by computational linguistic methods. In this work, we address this important … Read More

Online news clustering for crosslingual media monitoring

Scalable Understanding of Multilingual MediA (SUMMA) is an European Horizon 2020 research project which targets to develop a highly scalable platform to automatically monitor public broadcast and web-based news sources, enabling news agencies and journalists to cope with world-scale amounts … Read More

Microsoft machine learning story and why it is changing the company’s future

Microsoft started to work on machine learning a few years ago. We incubated a few solutions based in our online platforms and some internal systems. Today we are making them generally available to the public and changing all our products … Read More