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
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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
Collection and analysis of political discourse within the Portuguese parliamentary scenario
Within the context of text-as-data paradigm, new type of analysis and metrics are today available to the political science research, allowing new and different scientific approaches. Political science is an area particularly benefited with this new paradigm. Traditionally, all national … Read More
Generative Modelling of Single-cell Transcriptomic Data
Single-cell sequencing technology holds the promise of unravelling cell heterogeneities hidden in ubiquitous bulk-level analyses. However, limitations of current experimental methods pose new obstacles that prevent accurate conclusions from being drawn. To overcome this, researchers have developed computational methods which … Read More