S6 (2014-2015)

Towards Social Media Analysis for Low-Resource Languages

Modern web-based social networks have become platforms where individuals can express personal views and discuss relevant issues in real-time. The possibility of analysing this massive aggregation of thoughts and opinions has applications in several domains, ranging from finance and marketing … Read More

Noise Decomposition for Multiplicative and Wide Sense Multiplicative Noise

In some imaging modalities based on coherent radiation, the noise contaminating an image may contain useful information, thereby necessitating the separation of the noise field rather than just denoising. When the algebraic operation that relates the image and noise is … Read More

Minimum Number of Probes for Brain Dynamics Observability

In this talk, we address the problem of placing sensor probes in the brain such that the system dynamics’ are generically observable. The system dynamics whose states can encode for instance the fire-rating of the neurons or their ensemble following … Read More

NLP with characters

We present a neural network model that computes embeddings of words using recurrent network based on long short-term memories to read in characters. As an alternative to word lookup tables that require a set of parameters for every word type … Read More

Comparison of three methods for the identification of switched hybrid systems

This talk addresses the problem of parameter identification for switched ARX system. The identification of such systems typically results in non-convex optimization problems, where finding the globally optimal solution exhibits exponential computational complexity in the size of the input. The … Read More

Bottleneck Neural Network Language Models

In the last few years, language modeling techniques based on exponential models have consistently outperformed traditional n-gram models. Such techniques include L1-Regularized Maximum Entropy (L1-MaxEnt), and both Feedforward and Recurrent Neural Network Language Models (RNNLM). While more accurate, these models … Read More

Sparse Estimation with Strongly Correlated Variables

This talk considers the recently introduced ordered weighted L1 (OWL) regularizer for sparse estimation problems with correlated variables. We begin by reviewing several convex analysis results concerning the OWL regularizer, namely: that it is indeed a norm, its dual norm, … Read More

Comparison of predictive accuracy and descriptive power of Machine Learning algorithms through a case study involving maintenance of jet engines

Jet engines are complex machines, subject to demanding operating conditions, whose proper maintenance is critical to ensure high safety, maximum availability and minimum lifecycle costs. Therefore, dozens of sensors measure in real-time hundreds of engine parameters (temperature, pressure, vibration, shaft … Read More

Unbabel – Distributed Crowd Translation

Unbabel is a startup whose mission is to enable seamless, trustworthy written communication in different languages. The Unbabel platform combines a novel approach to machine translation with a community of bilinguals and freelance translators which results in human quality translations, … Read More

Hyperspectral Source Separation

Hyperspectral cameras acquire electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. This enhanced spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that … Read More