2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDSS-11.4
Paper Title EXPEDITING DISCOVERY IN NEURAL ARCHITECTURE SEARCH BY COMBINING LEARNING WITH PLANNING
Authors Farzaneh S. Fard, Vikrant Tomar, Fluent.ai, Canada
SessionSS-11: On-device AI for Audio and Speech Applications
LocationGather.Town
Session Time:Thursday, 10 June, 14:00 - 14:45
Presentation Time:Thursday, 10 June, 14:00 - 14:45
Presentation Poster
Topic Special Sessions: On-device AI for Audio and Speech Applications
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In our previous work, we introduced NASIL as an automated neural architecture search method with imitation learning. Time to discover optimal structures is a key concern in many AML solutions including NASIL. Here, we proposed an extended version called "GNASIL" to speed up the process. Similar to NASIL, GNASIL takes advantage of imitation learning to discover neural architectures for a given device specification. Unlike NASIL that used deep deterministic policy gradient method, GNASIL uses the soft-actor-critic to predict an optimal layer during its search. Furthermore, GNASIL employs a set of probing options and combines learning and planning options to sweep the search space faster. We investigated impact of such deliberative planning on decision making process on a speech recognition task. Reported results demonstrate that probing options in presence of imitation learning enables GNASIL algorithm to automatically learn suitable network structures with very competitive performance both in terms of speed of finding the optimal architectures and their accuracy while keeping computational footprint restrictions into consideration.