It’s a basic and simple (nothing fancy, no ads, no BS, good for on the go) way to stay up to date with current news in the AI world: https://allainews.com/

https://youtu.be/Ucp0TTmvqOE?t=6659

Introduction is quite basic, but there are some interesting insights from Andrej regarding their approach afterwards.

I am currently reading the paper “Memory Aware Synapses: Learning what (not) to forget” (https://arxiv.org/abs/1711.09601) and am trying to figure out how to compute the additional loss term. The weight importance matrix Ω of the current parameters θ is (as I understand) just the gradients of all individual weights. But how is Ω(θ – θ)) computed, specifically Ω(θ)). I tried looking through the official git repository, but was somehow not able to find the answer. Does anyone have experience with this approach?

Hi, community!
I am not very familiar with recommender systems and read in a couple of papers (and it makes sense) that recommender systems, especially cold start ones, can work based on classic ML classification algorithms. I am wondering if any state-of-the-art or relevant recsys’s use classifiers. Paper links would be very appreciated.

On academictorrents there’s half a dozen different imagenets. Which one’s the one people typically refer to as imagenet in papers? What is the relation between these sets?

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Some examples of the ones that I’m confused about:

• Imagenet Full (Fall 2011 release)
• ImageNet Large Scale Visual Recognition Challenge (V2017)
• ImageNet LSVRC 2012 Training Set (Object Detection)
• ImageNet LSVRC 2014 Training Set (Object Detection)
• ImageNet LSVRC 2012 Validation Set (Object Detection)
• ImageNet LSVRC 2013 Validation Set (Object Detection)