Machine Learning for Signal Processing

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Translational utility is the ability of certain biomedical imaging features to capture useful subject-level characteristics in clinical settings, yielding sensible descriptions and/or predictions for individualized treatment trajectory. An important step in achieving translational utility is to demonstrate the association between imaging features and individual characteristics, such as sex, age, and other relevant assessments, on a large out-of-sample unaffected population (no diagnosed illnesses). This initial step then provides a strong normative basis for comparison with patient populations in clinical settings.

 

 

Technical Committee: Bio Imaging and Signal Processing, Machine Learning for Signal Processing

Past Members

Technical Committee

Past Members

 

The following lists all the past chairs and members of the SPS Machine Learning for Signal Processing Technical Committee. 

*NOTE: Please scroll up/down and left/right within the Past Members window to view the full list. Also, view the full downloadable list (right-click, Save As to save file).

The Clarity Project will be organising a series of machine learning challenges for advancing hearing-aid signal processing and speech-in-noise perception modelling.

Technical Committee: Machine Learning for Signal Processing

EDICS

Technical Committee

EDICS

NOTE: The Technical Committee's EDICS list is derived from the Society's Unified EDICS list. You can view the Society's complete Unified EDICS ist and EDICS list approval process on the Unified EDICS page.

 

In this competition, you are tasked with developing a controller to enable a physiologically-based human model with a prosthetic leg to walk and run. You are provided with a human musculoskeletal model, a physics-based simulation environment OpenSim where you can synthesize physically and physiologically accurate motion, and datasets of normal gait kinematics. You are scored based on how well your agent adapts to the requested velocity vector changing in real time.

Technical Committee: Machine Learning for Signal Processing

In many real-world machine learning applications, AutoML is strongly needed due to the limited machine learning expertise of developers. Moreover, batches of data in many real-world applications may be arriving daily, weekly, monthly, or yearly, for instance, and the data distributions are changing relatively slowly over time. This presents a continuous learning or Lifelong Machine Learning challenge for an AutoML system.

Technical Committee: Machine Learning for Signal Processing

In this competition you can take on the role of an attacker or a defender (or both). As a defender you are trying to build a visual object classifier that is as robust to image perturbations as possible. As an attacker, your task is to find the smallest possible image perturbations that will fool a classifier.

Technical Committee: Machine Learning for Signal Processing

Policies & Procedures

Technical Committee

The technical committee consists of:

  • Chair (previous vice chair, voting, 2 year term)
  • Vice chair (elected, voting, 2 year term)
  • Past chair (previous chair, voting, 1 year term)
  • Members (elected, voting, 3 year term)
  • Associate members (appointed by chair, non-voting, term decided by chair)
  • Affiliate members (non-elected, non-voting, unlimited term)

Member election is conducted in the fall every year with the goal of replacing approximately 1/3 of the members.

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