I am doing some research on machine learning algorithms in the context of a seminar, which focuses on Energy-Based Modeling vs Deep Learning Modeling specifically in working with images. Now I know these are broad topics, but I was wondering what some specific similarities and differences are. I have read countless papers that say "EBMs have properties that make them better than DL in certain tasks" but I haven't really found a comprehensive motivation
- What is the problem that EBM is solving compared to DL?
- Why are we doing this?
I hope this is the right site for this question (I also checked theoretical CS), This brought me here.
update Based on my continued reading I believe, that in simple terms my notion of ebm vs DL is wrong. It is a framework that can incorporate deep learning and thus helps improve interpret ability of deep learning models (fundamentally they are black boxes after all!). It is another point of view - a different pair of glasses to see the same thing, if you will. An example would be, that the probabilities generated by probabilistic EBMs are actual probabilities and allow us to make statements about uncertainty (in contrast to the regular soft Mac output). Furthermore their generative nature allows for better calibration in classification and better out of distribution detection.
Maybe this might help or interest someone perusing this modeling framework