I am currently working on a project on an unbounded multi-task optimization problem. Essentially lets say my model outputs a tensor which leads to an SNR type loss (for people familiar with wireless communications jargon, the signal and interference vectors are columns of this tensor) and I would like to improve this SNR upto some required value. Do you guys have any suggestions on loss functions I could use? Rn I am trying out (model_output_snr - Req SNR)^2, basically an MSE loss wrt the required minimum snr. This doesn't change the fact that the problem itself is unbounded and unsupervised. I am new to this style of learning paradigm since I am used to having data with inputs and labels.
I tried a bunch of architectures to solve this problem but fundamentally I feel like the training losses are looking super erratic and not improving at all even after thousands of epochs.
Are there any precursors to this kind of ML technique, anything I should look out for? Really any help would be great at this point thanks! The problem itself is similar to a convex optimization problem statement, but the maximisation objective is non-convex due to inherent non-linearities in activation functions. Is there some theoretical limit on such kind of learning problems which make this approach (using ML instead of convex optimization) pointless in the first place?
VoyagerExpress t1_iszfebl wrote
Reply to [D] Simple Questions Thread by AutoModerator
I am currently working on a project on an unbounded multi-task optimization problem. Essentially lets say my model outputs a tensor which leads to an SNR type loss (for people familiar with wireless communications jargon, the signal and interference vectors are columns of this tensor) and I would like to improve this SNR upto some required value. Do you guys have any suggestions on loss functions I could use? Rn I am trying out (model_output_snr - Req SNR)^2, basically an MSE loss wrt the required minimum snr. This doesn't change the fact that the problem itself is unbounded and unsupervised. I am new to this style of learning paradigm since I am used to having data with inputs and labels.
I tried a bunch of architectures to solve this problem but fundamentally I feel like the training losses are looking super erratic and not improving at all even after thousands of epochs.
Are there any precursors to this kind of ML technique, anything I should look out for? Really any help would be great at this point thanks! The problem itself is similar to a convex optimization problem statement, but the maximisation objective is non-convex due to inherent non-linearities in activation functions. Is there some theoretical limit on such kind of learning problems which make this approach (using ML instead of convex optimization) pointless in the first place?