San Diego Convention Center
San Diego, California, United States
1 - 5 August 2021
Conference OP509
Applications of Machine Learning 2021
Conference Committee
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
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:

  • 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.
  • biometrics
  • face recognition
  • bias in security application.
  • 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.
  • 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
  • controlling multiparameter physics experiments
  • optical damage evaluation
  • big physics data
  • optical component manufacturing
  • 3D manufacturing
  • high energy physics facilities such as NIF and others
  • multiparameter optimization in large physics-based simulations.
Conference Committee
Conference Chairs
Conference Co-Chairs
Program Committee
Program Committee continued...
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