Adam Dejl

Orcid: 0009-0006-0274-4160

According to our database1, Adam Dejl authored at least 14 papers between 2021 and 2025.

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

Timeline

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Bibliography

2025
Heterogeneous graph neural networks with post-hoc explanations for multi-modal and explainable land use inference.
Inf. Fusion, 2025

XAI-Units: Benchmarking Explainability Methods with Unit Tests.
Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, 2025

Hidden Conflicts in Neural Networks and their Implications for Explainability.
Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, 2025

Argumentative Large Language Models for Explainable and Contestable Claim Verification.
Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

Identifying Query-Relevant Neurons in Large Language Models for Long-Form Texts.
Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024
Analyzing Key Neurons in Large Language Models.
CoRR, 2024

Argumentative Large Language Models for Explainable and Contestable Decision-Making.
CoRR, 2024

Contestable AI Needs Computational Argumentation.
Proceedings of the 21st International Conference on Principles of Knowledge Representation and Reasoning, 2024

2023
A Knowledge Distillation Approach for Sepsis Outcome Prediction from Multivariate Clinical Time Series.
CoRR, 2023

CAFE: Conflict-Aware Feature-wise Explanations.
CoRR, 2023

RadGraph2: Modeling Disease Progression in Radiology Reports via Hierarchical Information Extraction.
CoRR, 2023

RadGraph2: Modeling Disease Progression in Radiology Reports via Hierarchical Information Extraction.
Proceedings of the Machine Learning for Healthcare Conference, 2023

2022
Treatment-RSPN: Recurrent Sum-Product Networks for Sequential Treatment Regimes.
CoRR, 2022

2021
Argflow: A Toolkit for Deep Argumentative Explanations for Neural Networks.
Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021


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