ir. Johannes Deleu

Accelerating Hopfield network dynamics : beyond synchronous updates and forward Euler

Cédric Goemaere (UGent) , Johannes Deleu (UGent) and Thomas Demeester (UGent)

(2024) 1ST ECAI WORKSHOP ON MACHINE LEARNING MEETS DIFFERENTIAL EQUATIONS : FROM THEORY TO APPLICATIONS. In Proceedings of Machine Learning Research 255.

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Clinical reasoning over tabular data and text with bayesian networks

Paloma Rabaey (UGent) , Johannes Deleu (UGent) , Stefan Heytens (UGent) and Thomas Demeester (UGent)

(2024) ARTIFICIAL INTELLIGENCE IN MEDICINE, PT I, AIME 2024. In Lecture Notes in Artificial Intelligence 14844. p.229-250

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Journal Article
A1
open access

Few-shot out-of-scope intent classification : analyzing the robustness of prompt-based learning

Yiwei Jiang (UGent) , Maarten De Raedt (UGent) , Johannes Deleu (UGent) , Thomas Demeester (UGent) and Chris Develder (UGent)

(2024) APPLIED INTELLIGENCE. 54(2). p.1474-1496

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Journal Article
A1
open access

Learning to reuse distractors to support multiple choice question generation in education

Semere Kiros Bitew (UGent) , Amir Hadifar, Lucas Sterckx, Johannes Deleu (UGent) , Chris Develder (UGent) and Thomas Demeester (UGent)

(2024) IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES. 17. p.375-390

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Accelerating hierarchical associative memory : a deep equilibrium approach

Cédric Goemaere (UGent) , Johannes Deleu (UGent) and Thomas Demeester (UGent)

(2023) Associative Memory & Hopfield Networks in 2023 : NeurIPS 2023 workshop, Proceedings.

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Conference Paper
C1
open access

BioDEX : large-scale biomedical adverse drug event extraction for real-world pharmacovigilance

Karel D'Oosterlinck (UGent) , François Remy (UGent) , Johannes Deleu (UGent) , Thomas Demeester (UGent) , Chris Develder (UGent) , Klim Zaporojets, Arya Ghodsi, S Ellershaw, J. Collins and C. Potts

(2023) 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023), Proceedings.

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Conference Paper
C1
open access

Career path prediction using resume representation learning and skill-based matching

Jens-Joris Decorte (UGent) , Jeroen Van Hautte, Johannes Deleu (UGent) , Chris Develder (UGent) and Thomas Demeester (UGent)

(2023) RECSYS in HR 2023 : the 3rd Workshop on Recommender Systems for Human Resources (RecSys in HR 2023), Proceedings. In CEUR workshop proceedings 3490.

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Extreme multi-label skill extraction training using large language models

Jens-Joris Decorte (UGent) , Severine Verlinden, Jeroen Van Hautte, Johannes Deleu (UGent) , Chris Develder (UGent) and Thomas Demeester (UGent)

(2023) AI4HR & PES 2023 : International Workshop on AI for Human Resources and Public Employment Services, Proceedings. p.1-10

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Distractor generation for multiple-choice questions with predictive prompting and large language models

Semere Kiros Bitew (UGent) , Johannes Deleu (UGent) , Chris Develder (UGent) and Thomas Demeester (UGent)

(2023) RKDE2023 : the 1st International Tutorial and Workshop on Responsible Knowledge Discovery in Education, Proceedings.

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Conference Paper
C1
open access

Learning from partially annotated data : example-aware creation of gap-filling exercises for language learning

Semere Kiros Bitew (UGent) , Johannes Deleu (UGent) , A. Seza Doğruöz (UGent) , Chris Develder (UGent) and Thomas Demeester (UGent)

(2023) Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023). p.598-609

Academic Bibliography

ir. Johannes Deleu

Accelerating Hopfield network dynamics : beyond synchronous updates and forward Euler

Clinical reasoning over tabular data and text with bayesian networks

Few-shot out-of-scope intent classification : analyzing the robustness of prompt-based learning

Learning to reuse distractors to support multiple choice question generation in education

Accelerating hierarchical associative memory : a deep equilibrium approach

BioDEX : large-scale biomedical adverse drug event extraction for real-world pharmacovigilance

Career path prediction using resume representation learning and skill-based matching

Extreme multi-label skill extraction training using large language models

Distractor generation for multiple-choice questions with predictive prompting and large language models

Learning from partially annotated data : example-aware creation of gap-filling exercises for language learning

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