no code implementations • 4 May 2024 • Vadim Liventsev, Vivek Kumar, Allmin Pradhap Singh Susaiyah, Zixiu Wu, Ivan Rodin, Asfand Yaar, Simone Balloccu, Marharyta Beraziuk, Sebastiano Battiato, Giovanni Maria Farinella, Aki Härmä, Rim Helaoui, Milan Petkovic, Diego Reforgiato Recupero, Ehud Reiter, Daniele Riboni, Raymond Sterling
The use of machine learning in Healthcare has the potential to improve patient outcomes as well as broaden the reach and affordability of Healthcare.
1 code implementation • 27 Feb 2024 • Vadim Liventsev, Tobias Fritz
Reinforcement Learning in Healthcare is typically concerned with narrow self-contained tasks such as sepsis prediction or anesthesia control.
no code implementations • 20 Apr 2023 • Vadim Liventsev, Anastasiia Grishina, Aki Härmä, Leon Moonen
Current approaches to program synthesis with Large Language Models (LLMs) exhibit a "near miss syndrome": they tend to generate programs that semantically resemble the correct answer (as measured by text similarity metrics or human evaluation), but achieve a low or even zero accuracy as measured by unit tests due to small imperfections, such as the wrong input or output format.
1 code implementation • 16 Aug 2021 • Sander de Bruin, Vadim Liventsev, Milan Petković
Recently there have been many advances in research on language modeling of source code.
2 code implementations • 8 Feb 2021 • Vadim Liventsev, Aki Härmä, Milan Petković
Automatic programming, the task of generating computer programs compliant with a specification without a human developer, is usually tackled either via genetic programming methods based on mutation and recombination of programs, or via neural language models.
1 code implementation • 23 Jan 2021 • Vadim Liventsev, Aki Härmä, Milan Petković
Most state of the art decision systems based on Reinforcement Learning (RL) are data-driven black-box neural models, where it is often difficult to incorporate expert knowledge into the models or let experts review and validate the learned decision mechanisms.