ICPR 2020 Workshop: HDL
High-dimensional Deep Learning

Held in conjunction with ICPR 2021; Milan, Italy, January 10th-15th 2021.

Organizers

Bas van Stein, Michael Lew, Thomas Bäck and Markus Olhofer

Leiden University, The Netherlands and Honda Research Institute Europe.

URL: https://icpr-hddeeplearning.com

It is our pleasure to invite you to the 2021 ICPR Workshop HDL: High-dimensional Deep Learning.

Higher dimensional data such as 3D, video, and simulation data are a leading edge of pattern recognition research. With the growth of prevalent application areas such as 3D games, self-driving automobiles, automobile and airplane design, health monitoring and sports activity training, a wide variety of new sensors and simulation techniques have allowed researchers to develop feature description models beyond 2D. In this workshop, we will present an overview and key insights into the state of the art of higher dimensional features from a wide variety of techniques including but not limited to deep learning and also traditional approaches. For example, numerous current pattern recognition methods are using 3D information from the sensor (e.g. KINECT, LIDAR, MRI, …) or are using 3D in modeling and understanding the 3D world. Although higher dimensional data enhance the performance of methods on numerous tasks, they can also introduce new challenges and problems. The higher dimensionality of the data often leads to more complicated structures which present additional problems in both extracting meaningful content and in adapting it for current machine learning algorithms.

Topics of interest

We seek contributions on a range of topics related to this theme including but not limited to the following:

3D and 4D Deep Neural Architectures
3D and 4D Deep Transfer Learning
3D and 4D Deep Pattern Recognition
Learning 3D from single images
High-dimensional simulation data and modeling analytics
Fusion of high-dimensional features and classifiers
3D and 4D Deep Features (e.g. temporal images/video, MRI, LIDAR, etc.)
Strengths and weaknesses of DL vs traditional approaches
Neuroevolutionary approaches
Optimization in latent space
Hyperparameter optimization
Geometric deep learning approaches
Very high dimensional deep learning (5D+)
Adapting low dimensionality methods to higher dimensions
Dimensionality reduction
Benchmarks with traditional approaches
Important dates

Note that these dates are strict, no extensions will be granted:

· Submission deadline: June 15th 2020

· Workshop author notification: July 15th 2020

· Camera-ready submission: July 30th 2020

· Finalized workshop program: August 15th 2020

Submissions

All accepted papers are published in the Springer Workshop proceedings. There will be no printed version.

All papers should adhere to the Springer proceedings guidelines and templates.

https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines



Workshop papers must adhere to the following page limits:

· Full paper (12-15 pages)

· Short papers (6-8 pages)

Workshop papers must be submitted using the submission site (https://ocs.springer.com/ocs/home/HDL2020)

All accepted papers will be presented at the workshop and appear in the Springer Workshop proceedings. By submitting a paper, the author(s) agree that, if their paper is accepted, they will:

1. Submit a final, revised, camera-ready version to the publisher on or before the camera-ready deadline

2. Register at least one author before July 30th to attend the conference.

3. Attend the conference (at least one author).

4. Present the accepted paper at the conference.


Visit https://www.micc.unifi.it/icpr2020/ for more information.