Applications of Machine Learning 2021, Conference Details

San Diego Convention Center
San Diego, California, United States
1 - 5 August 2021

Conference OP509

Applications of Machine Learning 2021
Conference Committee

This conference has an open call for papers:
submit an abstract
(sign in required)
Submission guidelines for Authors and Presenters

Important Dates

Abstract Due:
3 February 2021
Post-Deadline Submissions are Currently Being Accepted.

Author Notification:
5 April 2021

Manuscript Due Date:
7 July 2021

Additional Conference Information

Call for Papers Flyer (346KB)

Call for
Papers

This conference provides a technical forum for members of both industry and academia to present their latest applications of machine learning. Machine learning has been applied to a broad domain of image/vision systems from medical imaging to consumer cameras. Learned tasks such as image recognition, noise reduction, or natural language processing, are currently being applied in many common devices such as mobile phones. Training datasets and training methods for machine learning are critical for the success of a system. Studies demonstrating the deployment and benchmarking of machine learning algorithms on specialized computer hardware is highly valuable to many groups in this field. Sensor hardware design or selection as it pertains to machine learning tasks; for example, an analysis of different camera designs and how each pertains to the performance of an image recognition task such as object detection, is of interest. Analysis of full systems that include the sensor technology, data processing hardware, and results are welcome as each area is critical for the successful application of machine learning.

Papers or tutorials reviewing the topics covered by this section are welcome. All abstracts will be reviewed by the program committee for originality and merit. Topics of interest include, but are not limited to, the following:

Algorithms

application or adaptation of known neural network and deep learning architectures
neural networks, deep learning, reinforcement learning, evolutionary, and spiking algorithms
computational intelligence, graphical models
optimization methods which better enable application of machine learning algorithms (mixed or lower precision, pruning, grouped convolutions, node fusion, etc.)
analysis and comparison of computational complexity, utility, speed, green AI
full system analysis of image sensor data, computer hardware, machine learning task, and results
training methods, such as transfer learning, meta learning, few shot learning, low shot learning, unsupervised, recurrent and self-supervised learning
data augmentation techniques
application of AR, VR, or MR to ML tasks
explainable AI, uncertainty quantification, salient image studies.

Consumer Application

application of ML algorithms to specific datasets, tasks, or hardware
image classification, detection, localization, segmentation
interpolation, denoising, clustering, super-resolution, image quality
hyperspectral target detection
biometrics
voice recognition, natural language processing
human/machine smart interface
tracking (global and local), generative models, and RF applications (classification, detection, spoofing, etc.)

Industrial Application

remote sensing
predictive and preventative maintenance
automotive safety
self-driving cars
machine inspection of industrial parts
agricultural, food safety
surveillance
optical manufacturing
3D manufacturing
cognitive agents and decision making
robotics.

Security

biometrics
face recognition
bias in security application.

Medicine

medical diagnostic and evaluation
drug/viral antibody design

Big Data

real-time processing of data using machine learning algorithms
Big Data applications of machine learning
medical data (tumor, cancer, longitudinal studies)
training datasets for machine learning
multi-sensor data and data fusion (radio frequency, EO, hyperspectral, IR)
meteorological
remote sensing data.

Hardware

application of machine learning algorithms on low SWaP computer hardware
sensor modalities (mobile phone, consumer camera, machine vision systems, automotive imaging systems, micro-pulse radar, x-ray, OCT, CAT, MRI, hyperspectral, image satellites, airborne imaging systems, astronomical observing telescopes)
image system or general sensor design and its relationship to machine learning tasks, methodologies for system characterization using machine learning tasks
selection of image system parameters and their relationship to machine learning tasks
real-time applications of machine learning.

Big Experimental Facilities