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] Coming up with new architectures

I was wondering about how people come up with new architectures for neural nets. I am currently working on an object detection problem, and the models that are SOTA are huge.

With models that have more than 500 layers and millions of parameters, how does one come up with an architecture that is better than the existing ones? I know that papers are mostly written around innovative ‘concept’ blocks (like skip connections or a feature pyramid block), so are researchers just iterating on all possible combinations of blocks to come up with an answer?

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


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.