Geometric deep learning is an emerging area of research in machine learning focusing on exploiting symmetries in problems to improve models. Its goal is to understand how transformations to the input should affect the output and design neural networks around … Read More
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Equivariant neural networks for recovery of Hadamard matrices
Geometric deep learning is an emerging area of research in machine learning focusing on exploiting symmetries in problems to improve models. Its goal is to understand how transformations to the input should affect the output and design neural networks around … Read More
Simplifying Multilingual News Clustering Through Projection From a Shared Space
The task of organizing and clustering multilingual news articles for media monitoring is essential to follow news stories in real time. Most approaches to this task focus on high-resource languages, with low-resource languages being disregarded. With that in mind, we … Read More
From Captions to Natural Language Explanations
The growing importance of the Explainable Artificial Intelligence (XAI) field has resulted in the proposal of several methods for producing visual heatmaps of the classification decisions of deep learning models. However, visual explanations are not enough since different end-users have … Read More
Revealing semantic and emotional structure of suicide notes
Understanding how people who commit suicide perceive their cognitive states and emotions represents a crucially open scientific challenge. We build upon cognitive network science, psycholinguistics, and semantic frame theory to introduce a network representation of suicidal ideation as expressed in … Read More
NLP at Cleverly and Multilingual Email Zoning
Cleverly is an end-to-end AI layer for customer service platforms that provides intelligent automation and efficiency and unlike others is easy to use. We reduce the effort agents spend on repetitive tasks and searching for the right information, giving them … Read More
Fact-checking as a conversation
Misinformation is considered one of the major challenges of our times resulting in numerous efforts against it. Fact-checking, the task of assessing whether a claim is true or false, is considered a key weapon in reducing its impact. In the … Read More
Visual Attention with Sparse and Continuous Transformations
Visual attention mechanisms have become an important component of neural network models for Computer Vision applications, allowing them to attend to finite sets of objects or regions and identify relevant features. A key component of attention mechanisms is the differentiable … Read More
Explainability for Sequential Decision-Making
Machine learning has been used to aid decision-making in several domains, from healthcare to finance. Understanding the decision process of ML models is paramount in high-stakes decisions that impact people’s lives, otherwise, loss of control and lack of trust may … Read More
A brief history of Spoken Language Understanding
Spoken Language Understanding consists in extracting semantic information conveyed by speech signal in order to project it into a representation manageable by a software application. This research topic encompasses several tasks like domain classification, named entity recognition, slot filling… By … Read More