ICPR 2020 Workshop on Computational Aspects of Deep Learning Call for Papers

ICPR 2020 Workshop on Computational Aspects of Deep Learning

Organized in conjunction with ICPR 2020

--- Submission deadline: 10 October 2020 ---

Deep Learning has been the most significant breakthrough in the past
10 years: it has radically changed the research methodology towards a
data-oriented approach, in which learning involves all steps of the
prediction pipeline. In this context, optimization and careful design
of neural architectures play an increasingly important role which
directly affects the research pace, the effectiveness of
state-of-the-art models and their applicability in production scale.

The ICPR workshop on "Computational Aspects of Deep Learning"
fosters the submission of research works that focus on the development
of optimized deep neural network architectures and on the optimization
of existing ones, also onto highly scalable systems. This includes the
training on large-scale or highly-dimensional datasets, the design of
novel architectures and operators for increasing the efficacy or the
efficiency in feature extraction and classification, the optimization
of hyperparameters to enhance model's performance, solutions for
training in multi-node systems such as HPC clusters.

The workshop targets any research field related to pattern
recognition, ranging from computer vision to natural language
processing and multimedia, in which data and computationally intensive
architectures are needed to solve key research issues. The workshop
also favors positive criticism on the current data-intensive trends in
machine learning and will encourage new perspectives and solutions on
the matter. Submissions should address computationally intensive
scenarios from the point of view of architectural design, data
preparation and processing, operator design, training strategies,
distributed and large-scale training. Quantitative comparisons of
existing solutions and datasets are also welcome to raise awareness on
the topic.

CADL is organized in collaboration with NVIDIA AI Technology
Center. The best paper will be awarded a Titan RTX GPU (or equivalent)
offered by NVIDIA.

Topics of interest include, but are not limited to, the following:

- Design of innovative architectures and operators for data-intensive scenarios
- Video understanding and spatio-temporal feature extraction
- Distributed reinforcement learning algorithms
- Applications of large-scale pre-training techniques
- Distributed training approaches and architectures
- HPC and massively parallel architectures in Deep Learning
- Frameworks and optimization algorithms for training Deep Networks
- Model pruning, gradient compression techniques to reduce the computational complexity
- Design, implementation and use of hardware accelerators

We invite submission of full and short papers describing work in the
domains suggested above or in closely-related areas.

Accepted submissions will be presented either as oral or posters at
the workshop, and published in the ICPR 2020 Workshops volume, edited
by Springer.

Full papers: 12-15 pages
Short papers: 6-8 pages

- Paper submission deadline: October 10th, 2020
- Notification of acceptance: November 10th, 2020
- Camera ready deadline: November 15th, 2020
- Finalized workshop program: December 1st, 2020

- Frederic Pariente, Engineering Manager at NVIDIA, deputy director of NVAITC in EMEA
- Iuri Frosio, Senior research scientist, NVIDIA
- Lorenzo Baraldi, Assistant Professor at UNIMORE
- Claudio Baecchi, Post-Doc at MICC, University of Florence

For further information, please see the workshop website at http://www.cadl.it/