Don't read them for the sake of reading them. Read them to solve your current problem or trying to keep up with advancements in a narrow field you love. Most pa

Don't read them for the sake of reading them. Read them to solve your current problem or trying to keep up with advancements in a narrow field you love. Most papers (especially the ones in deep learning) seem to also have a mathematical fetish (to put it mildly) where needless representations are used where none are required and are self evident (for example inputs belong to Real number set). It ends up making the paper pseudo complex and unapproachable. Most papers are doing average/summation/series operations but instead of just saying so, use the symbols all over the place. So even if a few papers appear tough, keep reading them and digest your first paper thoroughly. You will find subsequent papers mostly are a rehash of existing work with similar fetish to make trial and error appear like mathematically sound research. Once in a while, you would find some paper which is fully theoretical and try to prove that either the inputs/outputs/components of models have certain well known mathematical properties and hence can be reasoned similarly. These are rare and would be difficult to parse through. PS: Best papers I have seen are from deepmind where the approaches usually described are novel, varied and path breaking. Worst ones are - well no names but those that just use training and eval sets generated by GPT4 and try to prove things empirically