S10 (2018-2019)

INSIGHT: Advancing NLP for scaling-up Media Monitoring and Technology Watch

We’ll discuss how Priberam is applying machine learning to the problems of Media Monitoring and Technology Watch in the context of the INSIGHT P2020 project. We’ll show a working prototype and describe the developed microservices platform, and how the several … Read More

Learning of leader-follower graph from time-correlated big data streams with missing entries

Nowadays we are collecting high-dimensional and large data streams, where many dimensions can be expressing basically the same information on the underlying process of interest. This redundancy is apparent, for example, if we observe mass media news outputs through time. … Read More

DJAM – Distributed Jacobi Asynchronous Method for Learning Personalized Models

With the widespread of data collection agent networks, distributed optimization and learning methods become preferable over centralized solutions. Typically, distributed machine learning problems are solved by having the network’s agents aim for a common (or consensus) model. In certain applications, … Read More

A dynamical systems’ perspective of the expectation-maximization-like algorithms

The Expectation-Maximization (EM) algorithm is one of the most popular methods used to solve the problem of distribution-based clustering in unsupervised learning. In this talk, we propose a dynamical systems perspective of the EM algorithm. More precisely, we can analyze … Read More

Fraud Prevention with Deep Learning models

Feedzai is a scale up company with one mission: making banking and commerce safe. For that purpose, Feedzai develops methods for fraud prevention that should simultaneously be accurate, scalable and work within low latencies. In this talk will cover Feedzai’s … Read More

An optimization approach for structured agent-based provider/receiver tasks

This work contributes an optimization framework in the context of structured interactions between an agent playing the role of a ‘provider’ and a human ‘receiver’. Examples of provider/receiver interactions of interest include ones between occupational therapist and patient, or teacher … Read More

Transfer learning approach for fall detection with the FARSEEING real-world dataset and simulated falls

Falls are very rare and extremely difficult to acquire in free living conditions. Due to this, most of prior work on fall detection has focused on simulated datasets acquired in scenarios that mimic the real-world context, however, the validation of … Read More

Learning with Sparse Latent Structure

Structured representations are a powerful tool in machine learning, and in particular in natural language processing: The discrete, compositional nature of words and sentences leads to natural combinatorial representations such as trees, sequences, segments, or alignments, among others. At the … Read More

Robust Object Recognition Through Symbiotic Deep Learning In Mobile Robots

Despite the recent success of state-of-the-art deep learning algorithms in object recognition, when these are deployed as-is on a mobile service robot, we observed that they failed to recognize many objects in real human environments. In this paper, we introduce … Read More

Will Deep Convolutional Neural Networks open the way to Artificial General Intelligence?

Artificial Intelligence, and its diverse subfields, including machine learning, has been the subject of intense study for more than half a century. Recent advances in machine learning, jointly known as deep learning, have partially closed the gap that exists between … Read More