Oskar Wysocki

Orcid: 0000-0002-7053-4919

According to our database1, Oskar Wysocki authored at least 15 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Large Language Models, scientific knowledge and factuality: A framework to streamline human expert evaluation.
J. Biomed. Informatics, 2024

An LLM-based Knowledge Synthesis and Scientific Reasoning Framework for Biomedical Discovery.
CoRR, 2024

2023
A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.
BMC Bioinform., December, 2023

Meta-analysis informed machine learning: Supporting cytokine storm detection during CAR-T cell Therapy.
J. Biomed. Informatics, June, 2023

Transformers and the Representation of Biomedical Background Knowledge.
Comput. Linguistics, March, 2023

Assessing the communication gap between AI models and healthcare professionals: Explainability, utility and trust in AI-driven clinical decision-making.
Artif. Intell., March, 2023

Large Language Models, scientific knowledge and factuality: A systematic analysis in antibiotic discovery.
CoRR, 2023

On the Visualisation of Argumentation Graphs to Support Text Interpretation.
CoRR, 2023

2022
Biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.
CoRR, 2022

Metareview-informed Explainable Cytokine Storm Detection during CAR-T cell Therapy.
CoRR, 2022

2021
Architectures of Meaning, A Systematic Corpus Analysis of NLP Systems.
CoRR, 2021

What is SemEval evaluating? A Systematic Analysis of Evaluation Campaigns in NLP.
Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems, 2021

2020
What is SemEval evaluating? A Systematic Analysis of Evaluation Campaigns in NLP.
CoRR, 2020

2019
Heavy Duty Vehicle Fuel Consumption Modelling Based on Exploitation Data by Using Artificial Neural Networks.
Proceedings of the Advances in Computational Intelligence, 2019

Heavy duty vehicle fuel consumption modeling using artificial neural networks.
Proceedings of the 25th International Conference on Automation and Computing, 2019


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