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The Artificial Intelligence Laboratory of Udine (AILAB Udine) was founded in 1984 by Professor Carlo Tasso with the aim of producing research in the areas of Neural Networks, Artificial Intelligence, Machine Learning, Knowledge-based Systems, User Modeling, Personalization of Web sites and portals, Intelligent Interfaces for information retrieval and information filtering, Enterprise information portals for knowledge management, E-learning and information sharing. The laboratory has a 30 years long history of innovation and active support to local activities. Professor Carlo Tasso and Professor Giuseppe Serra co-lead the Laboratory .

Prospective PHD Students

If you are a prospective student interested in Artificial Intelligence, Machine Learning and Deep Learning Research at the University of Udine, please read about our Ph.D. admissions process and contact us (Giuseppe Serra – giuseppe.serra@uniud.it). If you are applying to our Ph.D. course in Computer Science and are interested in our research, please state this in your statement of purpose.

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, neural networks like Long Short-Term Memory (LTM) and Convolutional Neural Networks (CNN) have found many applications in this area, this because of their suitability to uncover hidden patterns within the sensor data. In recent years a greater availability of high quality sensors and easiness of data […]

Lecture 7th May 2020 – Beyond Hand-Crafted Networks:
Neural Architecture Search

Beyond Hand-Crafted Networks: Neural Architecture Search Dott. Stefano Alletto  Online lecture – Thursday, 07 May 2020, 08:30 a.m. (GMT+1) With the performance on several benchmarks approaching saturation, pushing the state of the art is often a tedious process of hyperparameter tuning and network architecture optimization. Finding the perfect neural network for a given task by hand is often impossible due to time constraints, but what if we could design a system capable of automatically designing architectures, test their performance and improve itself by looking at its previous mistakes?This is the goal of neural architecture search (NAS): an automated system that explores search spaces which size is beyond human capabilities, samples network structures from them and improves its decision making by using the performance of the architectures it finds as supervision. In this talk, after introducing this task more in detail, I will be giving an overview of recent NAS approaches, discussing the opportunities and limitations in the field. Finally, […]

Generalized Born radii computation using linear models and neural networks

Implicit solvent models play an important role in describing the thermodynamics and the dynamics of biomolecular systems. Key to an efficient use of these models is the computation of Generalized Born (GB) radii, which is accomplished by algorithms based on the electrostatics of inhomogeneous dielectric media. The speed and accuracy of such computations is still an issue especially for their intensive use in classical molecular dynamics. Here, we propose an alternative approach that encodes the physics of the phenomena and the chemical structure of the molecules in model parameters which are learned from examples. In our project, GB radii have been computed using i) a linear model and ii) a neural network. The input is the element, the histogram of counts of neighbouring atoms, divided by atom element, within 16 Å. Linear models are ca. 8 times faster than the most widely used reference method and the accuracy is higher with correlation coefficient with the inverse of “perfect” GB radii […]

Fake News Detection (AILAB-Udine – MIT Boston)

In the last few years, we have assisted at the explosion of news sharing and commenting in social networks. While this practice has positive aspects, as it stimulates the debate, it has been polluted by the diffusion of unreliable news, generally referred to as Fake News. Since these contents are often produced with malicious intents and they have a tremendous real-world political and social impact, the Natural Language Processing (NLP) community has been called to propose algorithms for their identification. Most of the currently existing works are so far based on stylistic and linguistic peculiarity of the Fake News texts (such as excessive use emphasis and hyperbolic expressions). As time passes, however, the Fake News tend to be stylistically and linguistically more similar to Real News, so that Fact Checking remains as the only reliable approach to isolate them. In this project, we employ Artificial Intelligence to assess the reliability of news on the base of not only intrinsic criteria […]