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Learning to Transfer with von Neumann Conditional Divergence

Learning paradigms designed for multiple domains or tasks, such as multitask learning, continual learning and domain adaptation, aim to reduce the large amount of energy and manual labor needed to retrain machine learning models. In this work, we introduce a domain adaptation approach that exploits learned features from relevant source tasks to reduce the data required for learning the new target task.

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Uncertainty Quantification in Node Classification

Modern neural networks are widely applied in a variety of learning tasks due to their exceptional performance, but fail to express uncertainty about predictions. For example, if a neural network is trained to predict whether an image contains a cat or a dog and is given an elephant as input, it will not admit that it is unsure. With a relatively high probability the machine learning model will instead still choose cat or dog. For high-risk domains like healthcare and autonomous driving this is not the best approach. In these areas, the cost and damage caused by overconfident or underconfident predictions can be catastrophic.

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NEC Student Research Fellowship Interview with Dr. Sergi Abadal

Dr. Sergi Abadal completed his NEC Student Research Fellowship with NEC Laboratories Europe on the topic, iGNNspector: Graph-Driven Acceleration of Graph Neural Networks. He recently received a 2022 European Research Council Starting Grant to continue his research.

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Advancing Disease Prevention with NEC MicrobiomePredict: Human Gut Microbiome Disease Prediction

Recent studies show that human gut microbiome plays a key role in regulating human health. Yet despite this, there are no clinical diagnostic tools that use microbiota to identify imbalances within our gut that can lead to serious disease, or tools for identifying existing disease. To help overcome this NEC have developed the machine learning model and system, NEC MicrobiomePredict, which analyzes a person’s gut microbiome to predict whether they are suffering from a disease.

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