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] Is there a way for a Neural Net (or other approach) to handle interchangeable features explicitly?

Let’s say I have 3 features describing some value of 3 objects of importance. The 3 objects are identical.

In my current model, the three objects are basically randomly assigned to be the features object_0, object_1, object_2.

It doesn’t matter for the real life outcome which object is 0, 1 , or 2. Of course, the neural net will learn that these are interchangeable in theory, but is there a way people have been doing this in a more explicit way?
Would it improve training to, for example, always order the values from largest to smallest, 0-2? Or some manipulation like that?

If this is a terrible question or you have corrections about the way I asked it, feel free to let me know. I am still learning.

🙂

Thanks

submitted by /u/HomieSapien
[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.