The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence

no code implementations • 9 Jul 2023 • Hector Zenil, Jesper Tegnér, Felipe S. Abrahão, Alexander Lavin, Vipin Kumar, Jeremy G. Frey, Adrian Weller, Larisa Soldatova, Alan R. Bundy, Nicholas R. Jennings, Koichi Takahashi, Lawrence Hunter, Saso Dzeroski, Andrew Briggs, Frederick D. Gregory, Carla P. Gomes, Jon Rowe, James Evans, Hiroaki Kitano, Ross King

Yet, AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.

Model Discovery

ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies

no code implementations • 14 Jul 2018 • Gustavo Correa Publio, Diego Esteves, Agnieszka Ławrynowicz, Panče Panov, Larisa Soldatova, Tommaso Soru, Joaquin Vanschoren, Hamid Zafar

The ML-Schema, proposed by the W3C Machine Learning Schema Community Group, is a top-level ontology that provides a set of classes, properties, and restrictions for representing and interchanging information on machine learning algorithms, datasets, and experiments.

BIG-bench Machine Learning

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

no code implementations • 12 Sep 2017 • Ivan Olier, Noureddin Sadawi, G. Richard Bickerton, Joaquin Vanschoren, Crina Grosan, Larisa Soldatova, Ross D. King

We first carried out the most comprehensive ever comparison of machine learning methods for QSAR learning: 18 regression methods, 6 molecular representations, applied to more than 2, 700 QSAR problems.

BIG-bench Machine Learning Meta-Learning +1