• We welcome Saida

    Saida has joined us at the AILAB in the last days. She is a PhD student from Egypt. Her interests are in Machine Learning and Deep Learning, and in Fuzzy Logic too. We wish Saida to have a good time in our group and to achieve great goals in her work.

  • A new member joins AILAB

    We are very pleased to welcome a new member of the AILAB. Giuseppe Lancioni joined us as a Ph.D. student whose main research focus will cover the Data Analysis and Natural Language Processing fields. Everyone in the AILAB team is looking forward to work with him and see him achieve great goals!

  • Predictive Maintenance

    The remaining useful life (RUL) estimation of a component is an interesting problem within the Prognostics and Health Management (PHM) field, which consists in estimating the number of time steps occurring between the current time step and the end of the component life. Being able to reliably estimate this value can lead to an improvement of the maintenance scheduling and a reduction of the costs associated with it. Data driven approaches are often used in the literature and they are the preferred choice over model-based approaches: in fact, not only they are easier to build, but the data over which they are built can be gathered easily in many industrial applications. During the last years, Long-Short Term Memory (LSTM) networks have found many applications in this area because they are suitable to deal with time-series data and because they inherently learn how to remember long-term dependencies, which can be really useful to uncover hidden patterns within the sensor data. Here […]

  • Visual Saliency Prediction

    When human observers look at an image, attentive mechanisms drive their gazes towards salient regions. Emulating such ability has been studied for more than 80 years by neuroscientists and by computer vision researchers, while only recently, thanks to the large spread of deep learning, saliency prediction models have achieved a considerable improvement. Data-driven saliency has recently gained a lot of attention thanks to the use of Convolutional Neural Networks for predicting gaze fixations. In this project we go beyond standard approaches to saliency prediction, in which gaze maps are computed with a feed-forward network, and we present a novel model which can predict accurate saliency maps by incorporating neural attentive mechanisms. The core of our solution is a Convolutional LSTM that focuses on the most salient regions of the input image to iteratively refine the predicted saliency map. Additionally, to tackle the center bias present in human eye fixations, our model can learn a set of prior maps generated with […]