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Topic models are useful tools for analyzing and interpreting the main underlying themes of large corpora of text. Most topic models rely on word co-occurrence for computing a topic, i.e., a weighted set of words that together represent a high-level semanti ...
In everyday life, eating follows patterns and occurs in context. We present an approach to discover daily eating routines of a population following a multidimensional representation of eating episodes, using data collected with the Bites'n'Bits smartphone ...
The use of smartphone sensing for public health studies is appealing to understand routines. We present an approach to learn nightlife routines in a smartphone sensing dataset volunteered by 184 young people (1586 weekend nights with location data captured ...
Methods of estimating the similarity between individual publications is an area of long-standing interest in the scientometrics community. Traditional methods have generally relied on references and other metadata, while text mining approaches based on tit ...
Many interesting applications emerged with the increasing popularity of deep learning. This project explored natural language processing and visualization techniques as well as two neural network architectures to classify ICOs. The first network focused on ...
Approaches for estimating the similarity between individual publications are an area of long -standing interest in the scientometrics and informetrics communities. Traditional techniques have generally relied on references and other metadata, while text mi ...
Most of the Natural Language Processing (NLP) algorithms involve use of distributed vector representations of linguistic units (primarily words and sentences) also known as embeddings in one way or another. These embeddings come in two flavours namely, sta ...
In this dissertation, I develop theory and evidence to argue that new technologies are central to how firms organize to create and capture value. I use computational methods such as reinforcement learning and probabilistic topic modeling to investigate thr ...
Music analysis, in particular harmonic analysis, is concerned with the way pitches are organized in pieces of music, and a range of empirical applications have been developed, for example, for chord recognition or key finding. Naturally, these approaches r ...
In modern-data analysis applications, the abundance of data makes extracting meaningful information from it challenging, in terms of computation, storage, and interpretability. In this setting, exploiting sparsity in data has been essential to the developm ...
