In the past decade, artificial intelligence has made remarkable progress, achieving feats like self-driving cars, defeating go-masters, and precise image categorisation through supervised deep learning with labelled data. However, a significant challenge is that deep learning models tend to be biased towards their training conditions and struggle in real-world situations that differ from their training… Continue reading Real-World Learning
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The power of cooperation in networks of learning agents
We study the power of cooperation in a network of communicating agents that solve a learning task. Agents use an underlying communication network to get information about what the other agents know. In the talk, we show the extent to which cooperation allows to prove performance bounds that are strictly better than the known bounds… Continue reading The power of cooperation in networks of learning agents
Introduction to Tropical Geometry and its Applications to Machine Learning
Tropical geometry is a relatively recent field in mathematics and computer science combining elements of algebraic geometry and polyhedral geometry. It has recently emerged in the study of deep neural networks (DNNs) and other machine learning systems. In this talk we will first summarise introductory ideas and tools of tropical geometry and its underlying max-plus… Continue reading Introduction to Tropical Geometry and its Applications to Machine Learning
Machine learning for medicine and healthcare
Medicine stands apart from other areas where machine learning can be applied. While we have seen advances in other fields with lots of data, it is not the volume of data that makes medicine so hard, it is the challenges arising from extracting actionable information from the complexity of the data. It is these challenges… Continue reading Machine learning for medicine and healthcare
Symbolic, Statistical, and Causal Representations
In machine learning, we use data to automatically find dependencies in the world, with the goal of predicting future observations. Most machine learning methods build on statistics, but one can also try to go beyond this, assaying causal structures underlying statistical dependencies. It turns out that causality can play a central role in addressing some… Continue reading Symbolic, Statistical, and Causal Representations
Digital Pathology: On the intersection of Computer Vision and Data Science
Due to the proliferation of whole-slide-imaging (WSI) digital scanners it is now possible to leverage computer vision, image analysis, and machine learning techniques, such as deep learning to process the digital pathology images in hopes to derive, diagnosis and prognosis markers. The convergence of digital imaging, data science and pathology gave rise to a new… Continue reading Digital Pathology: On the intersection of Computer Vision and Data Science