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R0:507c2e7fe07ef9a317eb4c7a51869fdc-Federated Learning: Issues in Medical Application

Federated Learning: Issues in Medical Application

In this presentation, the current issues to make federated learning flawlessly useful in the real world will be briefly overviewed. They are related to data/system heterogeneity, client management, traceability, and security. Also, we introduce the modularized federated learning framework, we currently develop, to experiment various techniques and protocols to find solutions for aforementioned issues. The framework will be open to public after development completes.

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R0:a4d69dca3fc7b4bb1e9ce3992edead0d-'Framework' basado en imágenes parametrizadas sobre ResNet para identificar intrusiones en 'smartwatches' u otros dispositivos afines

‘Framework’ basado en imágenes parametrizadas sobre ResNet para identificar intrusiones en ‘smartwatches’ u otros dispositivos afines

La continua aparición y mejora de dispositivos móviles en forma de ‘smartwatches’, ‘smartphones’ y otros dispositivos similares ha propicio un creciente y desleal interés en poner bajo la lupa y el control de los aplicativos a sus usuarios. De forma ofuscada por los fabricantes.

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Probabilistic Machine Learning: An Introduction

Probabilistic Machine Learning: An Introduction

«In this book, we will cover the most common types of ML, but from a probabilistic perspective. Roughly speaking, this means that we treat all unknown quantities (e.g., predictions about the future value of some quantity of interest, such as tomorrow’s temperature, or the parameters of some model) as random variables, that are endowed with probability distributions which describe a weighted set of possible values the variable may have.[…].».

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R0identifier_ 7029b309b114bdb1aa72b6fd0da905f3-Yann LeCun’s Deep Learning Course at CDS

Yann LeCun’s Deep Learning Course at CDS

This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. The prerequisites include: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.

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