Blog

Learn About Our Meetup

5000+ Members

MEETUPS

LEARN, CONNECT, SHARE

Join our meetup, learn, connect, share, and get to know your Toronto AI community. 

JOB POSTINGS

INDEED POSTINGS

Browse through the latest deep learning, ai, machine learning postings from Indeed for the GTA.

CONTACT

CONNECT WITH US

Are you looking to sponsor space, be a speaker, or volunteer, feel free to give us a shout.

[D] Increasing regularization during deep network training

I recently read a paper that suggested increasing weight decay, dropout rate, etc. (i.e., regularization parameters) while a deep network was training to avoid overfitting; however, I cannot remember the name of the paper. I tried to search through the literature, but searching using terms like “increase regularization deep learning” hasn’t turned up much (unsurprisingly).

I did find Curriculum Dropout, which suggests increasing the dropout rate during training, but I don’t believe this is the paper I had in mind.

Anyone happen to know of other papers discussing this subject? Are there any appearing trends surrounding changing regularization parameters during training? Anyone have any experience testing this idea out?

Thanks

submitted by /u/jcreinhold
[link] [comments]

Next Meetup

 

Days
:
Hours
:
Minutes
:
Seconds

 

Plug yourself into AI and don't miss a beat

 


Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.