"Quickly grok" to "test things out" - it just isn't going to happen like that, even if you know the math behind it (FWIW, for that paper you're going to need a

"Quickly grok" to "test things out" - it just isn't going to happen like that, even if you know the math behind it (FWIW, for that paper you're going to need a lot about statistics and Markov chains, but you may be able to get pretty far just by Googling the terms you don't know). Comp sci is notorious for having a huge gap between published work and working implementations. It often takes heroic efforts to put together a working version of the algorithms described, sometimes so much work that nobody has ever done it (Chazelle has some algorithms that have never been implemented in code). Even if it's plausible to do, don't be surprised if it takes weeks to get it working right. The more "on the edge" the algorithms are, the more difficult it tends to be, and it's not really likely that you'll be able to quickly experiment with new methods unless the authors have published code. This happens far less often than it should, because simple possession of a working algorithm is often enough to squeeze several more papers out of a topic, so authors have no particular incentive to share theirs, or even make it easy to create one from scratch. Computer graphics papers are particularly bad on that front, often omitting so much detail that even with significant study and effort you could never reproduce their results without researching the problem on your own anyways. Other people have offered good advice: go through it in excruciating detail, think about every equation as its own sub-problem, and make sure you understand what's happening at each step. These papers are meant for detailed study, not quick reading, even if the reader already knows a lot about the field. Google should help you through most of the math stuff, and try going to book search or the papers search if you're not finding anything online, which will give you good keywords to look for books on.