CALL FOR PAPERS
IEEE Journal of Selected Topics in Signal Processing Special Issue on
Compact Deep Neural Networks with Industrial Applications https://ift.tt/2Uazrwf
Artificial neural networks have been adopted for a broad range of
tasks in areas like multimedia analysis and processing, media coding,
data analytics, etc. Their recent success is based on the feasibility
of processing much larger and complex deep neural networks (DNNs) than
in the past, and the availability of large-scale training data sets.
As a consequence, the large memory footprint of trained neural
networks and the high computational complexity of performing inference
cannot be neglected. Many applications require the deployment of a
particular trained network instance, potentially to a larger number of
devices, which may have limitations in terms of processing power and
memory e.g., for mobile devices or Internet of Things (IoT) devices.
For such applications, compact representations of neural networks are
of increasing relevance.
This special issue aims to feature recent work related to techniques
and applications of compact and efficient neural network
representations. It is expected that these works will be of interest
to both academic researchers and industrial practitioners, in the
fields of machine learning, computer vision and pattern recognition,
media data processing, as well as fields such as AI hardware design
etc. In spite of active research in the area, there are still open
questions to be clarified concerning, for example, how to train neural
networks with optimal performance while achieving compact
representations, and how to achieve representations that do not only
allow for compact transmission, but also for efficient inference.
This special issue therefore solicits original and innovative works to
address these open questions in, but not limited to, following topics:
● Sparsification, binarization, quantization, pruning, thresholding
and coding of neural networks
● Efficient computation and acceleration of deep convolutional neural networks
● Deep neural network computation for low power consumption applications
● Exchange formats and industrial standardization of compact &
efficient neural networks
● Applications e.g. video & media compression methods using compressed DNNs
● Performance evaluation and benchmarking of compressed DNNs
Call for Papers: Joint Workshop on On-Device Machine Learning &
Compact Deep Neural Network Representations (ODML-CDNNR)
This joint workshop aims to bring together researchers, educators,
practitioners who are interested in techniques as well as applications
of on-device machine learning and compact, efficient neural network
representations. One aim of the workshop discussion is to establish
close connection between researchers in the machine learning community
and engineers in industry, and to benefit both academic researchers as
well as industrial practitioners. The other aim is the evaluation and
comparability of resource-efficient machine learning methods and
compact and efficient network representations, and their relation to
particular target platforms (some of which may be highly optimized for
neural network inference). The research community has still to develop
established evaluation procedures and metrics.
The workshop also aims at reproducibility and comparability of methods
for compact and efficient neural network representations, and
on-device machine learning. Contributors are thus encouraged to make
their code available.
Topics of interest include, but are not limited to:
. Model compression for efficient inference with deep networks and
other ML models
. Learning efficient deep neural networks under memory and compute
constraints for on-device applications
. Low-precision training/inference & acceleration of deep neural
networks on mobile devices
. Sparsification, binarization, quantization, pruning, thresholding
and coding of neural network
. Deep neural network computation for low power consumption applications
. Efficient on-device ML for real-time applications in computer
vision, language understanding, speech processing, mobile health and
automotive (e.g., . computer vision for self-driving cars, video and
image compression), multimodal learning
. Software libraries (including open-source) optimized for efficient
inference and on-device ML
. Open datasets and test environments for benchmarking inference with
efficient DNN representations
. Metrics for evaluating the performance of efficient DNN representations
. Methods for comparing efficient DNN inference across platforms and tasks
An extended abstract (3 pages long using ICML style, see https://ift.tt/2U7CANq ) in PDF
format should be submitted for evaluation of the originality and
quality of the work. The evaluation is double-blind and the abstract
must be anonymous. References may extend beyond the 3 page limit, and
parallel submissions to a journal or conferences (e.g. AAAI or ICLR)
Submissions will be accepted as contributed talks (oral) or poster
presentations. Extended abstract should be submitted through EasyChair
accepted abstracts will be posted on the workshop website and
Selection policy: all submitted abstracts will be evaluated based on
their novelty, soundness and impacts. At the workshop we encourage
DISCUSSION about NEW IDEAS.
Submission: Apr. 7, 2019
Notification: Apr. 24, 2019
Workshop: Jun. 14 or 15, 2019
Sujith Ravi, Google Research
Zornitsa Kozareva, Google
Lixin Fan, JD.com
Max Welling, Qualcomm & University of Amsterdam
Yurong Chen, Intel Labs China
Werner Bailer, Joanneum Research
Brian Kulis, Boston University
Haoji (Roland) Hu, Zhejiang University
Jonathan Dekhtiar, Nvidia
Yingyan Lin, Rice University
Diana Marculescu, Carnegie Mellon University
Does your research impact the medical field or applications like Virtual Reality and 3D printing, video and film production, or further enhance the study of color science?
Consider sharing your expertise as an oral or interactive paper, in the Journal of Imaging Science and Technology (JIST), or through a short course or workshop.
Your ticket awaits—Submit your research today for the Twenty-seventh
Color and Imaging Conference (CIC27)!
On 2019, the
IGS2019 International Geometry Summit features 4 major conferences in Computer
Graphics and applications
SPM – Solid and Physical
SIAM/GD Computational Geometric
SMI – Shape Modelling
GMP- Geometric Modelling and
IGS2019 seeks poster submissions, which describe recent work,
highly relevant results of work in progress, or successful systems and
applications, in all areas related to
solid and physical modelling
computational geometric design
shape modelling and analysis
geometric modelling and processing
poster is an important way to get feedback on work that has not yet been
published. Poster presentations will be an integral part of IGS2019, with a
joint poster session for interactive discussion between presenters and
attendees, plus a fast-forward presentation track where authors will orally
present a brief summary of their work to all IGS2019 attendees. We require at
least one presenter per accepted poster to attend IGS2019 or one of its 4
submissions must be in English and include authors names, affiliation, and
contact. The submission must be in the form of a two/four-page abstract, as a
PDF file with embedded fonts, and must be formatted according to the LATEX
guidelines available at the following URL
Hi Everyone- Here is a photo to add to the Jack Nachmias collection. It is from the late 1980s outside the Gleitman home on the occasion of changing of the chairmanship at Penn from Bob Rescorla to Jack. With Jack in the photo is Henry Gleitman and myself.
Photographer was Susan Rakowitz.
We are delighted to invite you to submit your work to the new Journal of Perceptual Imaging (JPI). JPI is an open-access, online publication, devoted to applied and fundamental research at the intersection of imaging and human vision, perception and cognition.
The journal focuses on perceptual and cognitive approaches to a wide range of imaging applications and technologies, and welcomes experimental, computational, theoretical, and survey papers. The goal is to be a source for multidisciplinary papers at the intersection of perception, imaging. and art, and for papers that are considered to be outside the scope of more narrowly-focused journals.
The inaugural issue has recently been published. These papers explore human spatial/temporal perception, computational photography, image quality models and applications, augmented reality, and color enhancement for color deficient users. A flyer about JPI and this first issue is attached below.
Why publish in JPI?
Your research is at the intersection of imaging and human vision, perception, or cognition
You’re looking for an open source journal
You want your work to be reviewed by multidisciplinary experts
You want to publish your work in a peer-reviewed journal and also present it at HVEI or one of the other IS&T conferences
You have presented a paper at a conference (e.g., HVEI, VSS, ECVP) and want to expand it into a full-length journal article
You want your paper to be read by an audience interested a wide range of topics related to human perception and cognition.
Click here to submit your manuscript to JPI.
Please send your comments or questions to us at: email@example.com