Matrix and Tensor Representations for Information Retrieval and Machine Learning (MTIRML) seminar is the opportunity to: 

• Learn about Matrix and Tensor decomposition approaches and their use in Information Retrieval(IR) and Machine Learning(ML)​ 

• Learn about the scientific process​ 

• Learn about the scientific process in computer science

Submission Guidelines

We invite the submission of original works that are related -- but are not limited to -- the topics below.

Topics of interests:

• Foundations in Linear Algebra​

  • Dimensionality Reduction​

  • Non-negative Matrix Factorizations​

  • Tensor Analysis and Decomposition

• Tensor Representations for Graphs and​ Knowledge Graphs

  • Tensors for Linked Data

  • Multiple Graphs Clustering​

  • Representing Evolving Graphs​

  • Graph Stores

• Information Retrieval​

• Tensor and matrix applications ​

  • Tensor and matrix applications (Recommender systems)​

  • Tensor and matrix applications ​ ​

Submission:

The workshop is open to original papers of theoretical or practical nature. Papers should be formatted according to LNCS instructions for authors. We welcome full papers 10-12 pages including references​. Papers have to be submitted via the seminar's Easychair submission page.

Committees

Program Committee

      TBD

Organizing committee

• Prof. Steffen Staab
• Dr. Rafika Boutalbi

Contact

 Dr. Rafika Boutalbi, Analytic computing, IPVS, University of Stuttgart