carbocation
carbocation t1_iz8n4ds wrote
carbocation t1_iybb5a8 wrote
Reply to comment by eternalmathstudent in Building ResNet for Tabular Data Regression Problem by eternalmathstudent
Yes, which is why I think you’ll find that link of particular interest since they comment on it (and attention).
carbocation t1_iyb3f36 wrote
While convolution is a bit funky with tabular data (what locality are you exploiting?), I think that attention is a mechanism that might make sense in the deep learning context for tabular data. For example, take a look at recent work such as https://openreview.net/forum?id=i_Q1yrOegLY (code and PDF linked from there).
carbocation t1_iy12bhd wrote
Reply to comment by naccib in [R] QUALCOMM demos 3D reconstruction on AR glasses — monocular depth estimation with self supervised neural network processed on glasses and smartphone in realtime by SpatialComputing
I agree with you about the value and use-cases for monocular depth estimation. I was just making the point that, in principle, a binocular device could attempt binocular depth estimation. Or perhaps they tried it internally and it was not sufficiently better to be worth the expense.
carbocation t1_ixyz3g4 wrote
Reply to [R] QUALCOMM demos 3D reconstruction on AR glasses — monocular depth estimation with self supervised neural network processed on glasses and smartphone in realtime by SpatialComputing
Seems like binocular depth estimation should be possible with a binocular device.
carbocation t1_j103ehe wrote
Reply to [D] Techniques to optimize a model when the loss over the training dataset has a Power Law type curve. by Dartagnjan
Have you tried focal loss? If I’m reading you correctly it’s appropriate for this type of question, although if the hard samples are distributed evenly across classes it is probably not actually going to help. I don’t think you mention what type of problem you’re solving (classification, regression, segmentation, etc) so it’s hard to guess.