BNAIC/BeNeLearn 2023

BNAIC/BeNeLearn is the reference AI & ML conference for Belgium, Netherlands & Luxembourg. The combined conference will take place from November 8th till November 10th in Delft, The Netherlands and is organized by the Delft University of Technology, under the auspices of the Benelux Association for Artificial Intelligence (BNVKI) and the Netherlands Research School for Information and Knowledge Systems (SIKS).

Call For Papers: BNAIC/BeNeLearn 2023

BNAIC/BeNeLearn is the reference AI & ML conference for Belgium, Netherlands & Luxembourg. The combined 35th BNAIC and 32nd BeNeLearn conference will take place November 8-10 in Delft, the Netherlands. It is organized by the Delft University of Technology, under the auspices of the Benelux Association for Artificial Intelligence (BNVKI) and in cooperation with the Netherlands Research School for Information and Knowledge Systems (SIKS).
SUBMISSION INFORMATION

Researchers are invited to submit unpublished original research on all aspects of Artificial Intelligence and Machine Learning. Additionally, high-quality research results already published in international AI/ML conference proceedings or journals are also welcome for presentation at the conference, and will be published as extended abstracts.

Four types of submissions are invited:

★ Type A: Regular papers Papers presenting original work that advances Artificial Intelligence and Machine Learning. Position and review papers are also welcome. These contributions should address a well- developed body of research, an important new area, or a promising new topic, and provide a big picture view. Type A papers can be long (up to 15 pages, excluding references and appendices) or short (at most 10 pages, excluding references and appendices). Contributions will be reviewed on the basis of their overall quality and relevance.

★ Type B: Encore abstracts Abstracts of work published (or accepted) in an international conference or journal relating to AI/ML and closely related fields. These should have been accepted on or after 1st September 2022. Authors are invited to submit the authors’ version of their officially published paper together with an abstract of at most 2 pages (excluding references). Authors are encouraged to include further results obtained after the publication in their abstract and presentation. Submissions will be judged based on their relevance to the conference. Authors may submit at most one type B paper of which they are the corresponding author.

★ Type C: Demonstration abstracts Proposals for demos should be submitted as a 2-page (excluding references) abstract. Demonstrations should also submit a short video illustrating the working of the system (not exceeding 15 minutes). Any system requirements should be mentioned in the submission. Demonstrations will be evaluated based on their originality and innovative character, the technology deployed, the purpose of the systems in interaction with users and/or other systems, and their economic and/or societal potential.

★ Type D: Thesis abstracts Bachelor and Master students are invited to submit a 2-page abstract (excluding references) of their completed AI/ML-related thesis. Supervisors should be listed. The thesis should have been accepted on or after 1st September 2022. Submissions will be judged based on their originality and relevance to the conference.

Reviews will be single-blind. All submissions should include author names and their affiliations.

Presentation and prizes

Type A, B and D papers can be accepted for either oral or poster presentation.

There will be prizes for the best paper (type A), best demonstration (type C), and best thesis (type D).

Pre-& post-proceedings

Accepted contributions in all four categories will be included in the (non-archival) conference pre- proceedings, published open access by TU Delft OPEN Publishing. All contributions should be written in English, using the Springer CCIS/LNCS format (see Information) and submitted electronically via EasyChair: EasyChair submit

Submission implies willingness of at least one author to register for BNAIC/BeNeLearn 2023 and present in person at the conference. For each paper, a separate author registration is required.

Similar to previous years, we plan to organize post-proceedings in the Springer CCIS series. A selection of type A papers will be invited to submit to the post-proceedings (https://urldefense.com/v3/__https://www.springer.com/series/7899__;!!PAKc-5URQlI!-zrgHLdunWtiq1FLZOjRF14Huh2oFUrfGQ9jCY5A6YbQnrGOvOIpXM75V72lGb2_fszpxigUWNpU7tYp_AIUgVw0TdQ9iZ_kdA$ ).

Important dates

All deadlines are at 23:59, Anywhere on Earth time zone.

   EXTENDED Abstract submission deadline: 30 August 2023
   EXTENDED Paper submission deadline: 4 September 2023
   Author notification: 27 September 2023
   Camera ready submission deadline: 17 October 2023
   Conference: 8-10 November 2023

Topics of Interest

We invite contributions on any topic in the broad area of Artificial Intelligence and Machine Learning. In addition to fundamental work we encourage cross-domain and interdisciplinary work, as well as application of AI or ML-based techniques. A non-exhaustive list of topics includes:

   Bayesian Learning
   Case-based Learning
   Causal Learning
   Clustering
   Computational Creativity
   Computational Learning Theory
   Computational Models of Human Learning
   Data Mining & Knowledge Discovery
   Data Visualisation
   Deep Learning
   Dimensionality Reduction
   Ensemble Methods
   Evaluation Frameworks
   Evolutionary Computation
   Graph Mining & Social Network Analysis
   Inductive Logic Programming
   Interactive AI / Human-in-the-loop Methods and Systems
   Kernel Methods
   Learning and Ubiquitous Computing
   Learning in Multi-Agent Systems
   Learning from Big Data
   Learning from User Interactions
   Media Mining and Text Analytics
   ML Applications in Industry
   ML for Scientific Discovery
   Natural Language Processing / Natural Language Understanding
   Online Learning
   Pattern Mining
   Ranking / Preference Learning / Information Retrieval
   Reinforcement Learning
   Representation Learning
   Robot Learning
   Social Networks
   Speech Recognition
   Structured Output Learning
   Time series modelling & prediction
   Transfer and Adversarial Learning