37 citations · 63 across the 5 of their papers we have counts for
5 papers
We Need Improved Data Curation and Attribution in AI for Scientific Discovery
Mara Graziani, Antonio Foncubierta, Dimitrios Christofidellis +5
As the interplay between human-generated and synthetic data evolves, new challenges arise in scientific discovery concerning the integrity of the data and the stability of the mode…
Multiscale Byte Language Models -- A Hierarchical Architecture for Causal Million-Length Sequence Modeling
Eric Egli, Matteo Manica, Jannis Born
Bytes form the basis of the digital world and thus are a promising building block for multimodal foundation models. Recently, Byte Language Models (BLMs) have emerged to overcome t…
Domain-agnostic and Multi-level Evaluation of Generative Models
Girmaw Abebe Tadesse, Jannis Born, Celia Cintas +4
While the capabilities of generative models heavily improved in different domains (images, text, graphs, molecules, etc.), their evaluation metrics largely remain based on simplifi…
Unifying Molecular and Textual Representations via Multi-task Language Modelling
Dimitrios Christofidellis, Giorgio Giannone, Jannis Born +3
The recent advances in neural language models have also been successfully applied to the field of chemistry, offering generative solutions for classical problems in molecular desig…
Accelerating Material Design with the Generative Toolkit for Scientific Discovery
Matteo Manica, Jannis Born, Joris Cadow +21
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful represe…