The function looks like this.pic.twitter.com/SqEK4696HW
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The well known L2 loss has issues when there are outliers in the datapic.twitter.com/WWisAVNagW
all animations are from author's talkhttps://drive.google.com/file/d/1GzRYRIfLHvNLT_QwjHoBjHkBbs3Nbf0x/view?usp=sharing …
Robust functions are useful in regression because they can cull the outliers from the optimisation process. Since the alpha is a free parameter they can learn that.pic.twitter.com/GjRnhNLcs2
alpha is robustness "we can even allow for the robustness of our loss to be adapted individually for each dimension of our output space — we can have a different degree of robustness at each pixel in an image, for example"
@egrefen I just remembered our discussion about loss functions so this paper may be of some possible interest.
In practice is alpha fixed or optimized during learning?
optimised, you can backprop through this.
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