SOURCE-LINKED INTELLIGENCE

Discovering the World Through Unsupervised Statistical Relational Learning

CORDIS · observation · Publication date unknown

Discovering the World Through Unsupervised Statistical Relational Learning Machine learning is popular nowadays, thanks to the impressive results achieved by systems like DeepMind’s AlphaGo, OpenAI’s language prediction model GPT-3 or Amazon’s speech recognition system Alexa. At the basis of these successes, there is representation learning, which enables training deep neural networks in an unsupervised fashion and provides the starting conditions for subsequent task-specific training. However, current representation learning strategies use large neural networks and consume large amount of data, thus being data and energy inefficient. In contrast, humans learn from limited data in a very efficient way. This is due to the fact that humans are able to perform reasoning, while representation learning strategies lack such capability. This research project aims to overcome these limitations by providing the mathematical foundations for the integration between unsupe

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

177322.08

unit

EUR