In the realm of machine learning, training accurate and robust models is a constant pursuit. However, two common challenges that often hinder model performance are overfitting and underfitting.

Molecular machine learning (ML) underpins critical workflows in drug discovery, material science, and catalyst optimization ...

The ideal model should be balanced. Overfitting in Machine Learning Overfitting is also a factor in machine learning. It might emerge when a machine has been taught to scan for specific data one ...

We take the opportunity to unpack what this means, and how it's related to the future of graph databases, as well as revisit interesting developments in Neptune's support for machine learning and ...

We take the opportunity to discuss the database market, graph, and beyond, with CEO and co-founder Claudius Weinberger and Head of Engineering and Machine Learning Jörg Schad. ArangoDB was ...

Scientist Yi Nian is sharing his machine-learning expertise with the world in his latest co-authored publication, “Globally Interpretable Graph Learning via Distribution Matching.” SEATTLE ...

The core product is a knowledge graph they claim has mapped “over ... they have released a short report about the state of the machine learning industry. The key slide I saw in the report ...

Year-to-date through September, Euclidean Fund I was up 9.8% net of fees and expenses in the context of the S&P 500 delivering a 10.6% total return, ...

Her research focuses on developing innovative algorithms and models that push the boundaries of machine learning, optimization, and artificial intelligence.