Important Dates

Submission Deadline: May 10, 2024
Acceptance Notification: May 25, 2024
Workshop Date: June 25, 2024

Submission Guidelines

The workshop welcomes the proposals for poster presentation (default), full paper (optional), or oral presentation (optional) that can be original research work, preliminary research findings, discussions on emerging directions, or highlights of significant published works. Below are the submission guidelines:

Submission Requirements:

  • Title: Create a concise yet descriptive title that effectively represents your AI research within Tensor Models, setting the tone for your presentation.
  • Authors: Include the names of all authors along with their affiliations.
  • Abstract: Provide a succinct abstract (150-250 words) that elaborates on your research's objectives, methodology, discoveries, and its impact. Emphasize how your work contributes to the evolving domains of resilience, scalability, and generative AI models.
  • Poster presentation (default): Authors have an option to submit a poster (if finished) along with their proposal. If you opt for a poster, it is encouraged to present your research's core concepts, methodologies, and results in an engaging format. Please note that if your proposal is accepted, presenting a poster during the workshop is mandatory by default.
  • Full Papers (optional): There is an option to submit a full paper. Accepted papers will be recommended for publication in the Springer Book Series on Adaptation, Learning, and Optimization (see Springer Series for details). For template and format, please refer to the guidelines provided in the "Information for Authors" section under Springer Computer Science Proceedings (Springer Guidelines). The accepted paper will have a poster and/or oral presentation.
  • Oral presentation (optional): The authors can choose whether they would like to give an oral presentation during submission. However, the final selection of oral presentation depends on the number of submissions. After the proposal is selected for an oral presentation, authors can choose not to give a poster presentation if they prefer.

Submission site:

Note: Currently, the only submission of title, authors, and abstract is acceptable for review. It is preferable for the authors to submit a poster or a full paper for review, but it is not mandatory.

Topics of Interest

We are soliciting contributions that address a wide range of theoretical and practical issues including, but not limited to:

  • Tensor solutions to trustworthy machine learning issues (robustness, interpretability, fairness, privacy, etc.)
  • Tensor methods for large language models
  • Fundamental theory for tensor decompositions/networks in machine learning
  • Tensor networks for supervised, unsupervised, and self-supervised machine learning
  • Tensor networks for dimensionality reduction, quantum-enhanced feature extraction, and topological structure learning
  • Tensor networks for probabilistic/graphical modeling, generative models, quantum many-body systems
  • Tensor network layer for compressing/accelerating deep neural networks
  • Tensor network solutions to Ising model and Ising formulation of optimization problems
  • Quantum machine learning algorithms via tensor networks
  • Quantum understanding of classical machine learning
  • Automatic differentiable programming for quantum tensor networks in machine learning